Skip to main content
Full access
Special Articles
Published Online: 29 November 2017

Complex Dynamics in the Basal Ganglia: Health and Disease Beyond the Motor System

Publication: The Journal of Neuropsychiatry and Clinical Neurosciences

Abstract

The rate and oscillatory hypotheses are the two main current frameworks of basal ganglia pathophysiology. Both hypotheses have emerged from research on movement disorders sharing similar conceptualizations. These pathological conditions are classified either as hypokinetic or hyperkinetic, and the electrophysiological hallmarks of basal ganglia dysfunction are categorized as prokinetic or antikinetic. Although nonmotor symptoms, including neurobehavioral symptoms, are a key manifestation of basal ganglia dysfunction, they are uncommonly accounted for in these models. In patients with Parkinson’s disease, the broad spectrum of motor symptoms and neurobehavioral symptoms challenges the concept that basal ganglia disorders can be classified into two categories. The profile of symptoms of basal ganglia dysfunction is best characterized by a breakdown of information processing, accompanied at an electrophysiological level by complex alterations of spiking activity from basal ganglia neurons. The authors argue that the dynamics of the basal ganglia circuit cannot be fully characterized by linear properties such as the firing rate or oscillatory activity. In fact, the neuronal spiking stream of the basal ganglia circuit is irregular but has temporal structure. In this context, entropy was introduced as a measure of probabilistic irregularity in the temporal organization of neuronal activity of the basal ganglia, giving place to the entropy hypothesis of basal ganglia pathology. Obtaining a quantitative characterization of irregularity of spike trains from basal ganglia neurons is key to elaborating a new framework of basal ganglia pathophysiology.
Models of basal ganglia physiopathology are traditionally founded on sensorimotor functions of this circuit.1 However, the role of the basal ganglia has been dramatically extended into nonmotor domains during the past decades.2 The basal ganglia play a critical role in cognitive functions, such as language, attention, memory and executive function, and other nonmotor domains.3,4 Indeed, disorders of the basal ganglia produce a combination of sensorimotor, autonomic, cognitive, and psychiatric symptoms.512 The high incidence of cognitive deficits and/or neuropsychiatric symptoms in basal ganglia disorders reflects the multiple functions of the basal ganglia in these nonmotor domains.8,9,13 An obvious example is Gilles-de-la-Tourette syndrome, which is both a neuropsychiatric disorder and a movement disorder. However, this is not an isolated case.
Most basal ganglia disorders have an impact on the three main categories of neuropsychiatric symptoms: cognition, emotion, and behavior. A majority of patients with either Parkinson’s disease, Huntington’s disease, Gilles-de-la-Tourette syndrome, supranuclear palsy, or corticobasal degeneration1418 exhibit neuropsychiatric symptoms. Ninety percent of parkinsonian patients and nearly all patients with Huntington’s disease (98%) exhibit at least one neuropsychiatric manifestation.17,19 While most movement disorders, including Parkinson’s disease, involve extrabasal ganglia lesions, imaging studies have shown a correlation between basal ganglia lesions and cognitive as well as noncognitive neuropsychiatric symptoms.20 Affective, apathetic and cognitive symptoms, including obsessive-compulsive disorder (OCD), suggest that basal ganglia lesions reduce the connectivity of the prefrontal and frontal cortex (connectivity disconnection syndrome), and thus lead to executive dysfunction.2123 Another key symptom of Parkinson’s disease, supranuclear palsy and Huntington’s disease is loss of motivation, which could be linked to pathological activity in the input nuclei (the striatum) and/or the output nuclei of the basal ganglia (the globus pallidus pars interna [GPi]).2426 Additionally, in parkinsonian patients, the incidence of neuropsychiatric symptoms correlates with cognitive deterioration.19,27 About half of these patients suffer depression, sometimes as a prodromal manifestation, which is often associated with faster worsening of cognitive and motor functions.2830 Less frequently, anxiety can dominate the neuropsychiatric spectrum in parkinsonian patients, while a smaller fraction of this population develops psychosis with hallucinations and delusions.19,31 Although a more detailed description is out of the scope of this review, it is worth noticing that basal ganglia dysfunction is also related to bipolar disorder, schizophrenia, mania, and delusion.3235

Overview of Basal Ganglia Circuitry

The basal ganglia (Figure 1) are part of the cortico-subcortical circuits and the networks to which they contribute.1,36 The striatum is the most prominent input nucleus to the basal ganglia and receives dense excitatory (glutamatergic) topographic projections from virtually all cortical areas. The striatum can be subdivided into a ventral part (nucleus accumbens), the dorsomedial striatum ([DMS] or caudate), and the dorsolateral striatum ([DLS] or putamen). The putamen receives sensorimotor and thalamic projections, while the nucleus accumbens and the caudate receive input from cognitive and limbic areas including those from the frontal cortex, prefrontal cortex, the insula, and temporal areas.37 The subthalamic nucleus (STN) is a second input nucleus to the basal ganglia, which also receives topographic projections from most cortical areas.38 The topographic organization of projections between nuclei is a critical anatomical feature of the cortico-basal ganglia-thalamo-cortical (Cx-BG-Th-Cx) circuits. An effect of this organization is the functional segregation of cortical downstream, although cross-talk between parallel pathways is also present.39
FIGURE 1. Schematization of the Basal Ganglia Circuita
a The basal ganglia network includes the striatum, the globus pallidus externa (GPe) and globus pallidus interna (GPi), the substantia nigra (pars substantia nigra compacta [SNc] and pars substantia nigra reticulata [SNr]), and the subthalamic nucleus (STN). These subcortical nuclei are interconnected and form a part of the frontal-subcortical loops (see Gatev et al.1). The striatum is a prominent input nucleus to the basal ganglia, which receives glutamatergic projections from virtually every cortical area and dopaminergic inputs from the SNc. The cortex exerts an excitatory influence over striatal efferent neurons (medium spiny neurons [MSNs]), which project to the basal ganglia output nuclei (GPi and SNr) via two pathways: The “direct” pathway is a GABAergic monosynaptic projection (striato-GPi/SNr), and the indirect “pathway” is a GABAergic multisynaptic projection (striato-GPe-GPi/SNr [see Frank280]). These two pathways arise from two populations of MSNs, expressing different dopaminergic receptors. While the MSNs of the direct pathway are under excitatory modulation from D1 [D1-like dopamine receptors] family receptors, the MSNs of the indirect pathway are under inhibitory influence from D2 [D2-like dopamine receptors] family receptors. The output nuclei of the basal ganglia are also under excitatory influence arising from the cortex via a relay in the subthalamic nucleus (STN, hyperdirect pathway) (see Nambu279). White arrows represent inhibitory GABAergic projection, black arrows represent excitatory glutamatergic projection, and the gray arrow represents dopaminergic projection. Abbreviations: DA=dopamine; Th=thalamus.
Regarding sensorimotor channels, somatotopic organization of cortical projections to the striatum and STN is maintained along Cx-BG-Th-Cx circuits.39 These circuits are characterized by consecutive segments of inhibitory (GABAergic) and excitatory (glutamatergic) projections between nuclei, driving cortical neuronal activity downstream. Briefly, the cortical glutamatergic drive exerts excitatory control over striatal efferent neurons (medium spiny neurons [MSN]), which project to the basal ganglia output nuclei via two pathways.40 The direct pathway is a GABAergic monosynaptic projection, while the indirect pathway is a GABAergic multisynaptic projection. At the level of the striatum, these two pathways are mostly segregated in two populations of MSN, expressing mostly different types of dopaminergic receptors.41 The MSN of the direct pathway express D1 dopaminergic receptors, the activation of which by dopamine has a facilitatory effect on these neurons. In contrast, the MSN of the indirect pathway express D2 dopaminergic receptors, the activation of which by dopamine has an inhibitory effect on these neurons. In human and nonhuman primates, the output nuclei of the basal ganglia consist of the GPi and the pars substantia nigra reticulata (SNr). The GPi receives its major inputs from the striatum.40 Additionally, the hyperdirect pathway bypasses the striatum, conveying excitatory impulses from the cortex to the globus pallidus through the STN.42
This anatomic-functional model stresses the importance of the balance of activity between the direct, indirect, and hyperdirect pathways on basal ganglia processing.1 In conditions with lesion of the dopaminergic nigrostriatal pathway (i.e., parkinsonism), the model predicts that loss of dopamine results in an imbalance in favor of the indirect pathway, leading to decreased inhibition and increased neuronal firing activity in the output basal ganglia.1,43 Secondarily, this imbalance leads to an inhibition of the thalamus and cortex.44 Excitation and inhibition of the thalamus are traditionally considered to promote the selection and inhibition of movement, respectively; therefore, pathologic thalamic inhibition produces akinetic symptoms, according to this model.1
While traditional models of the basal ganglia have shown extraordinary resilience to time, anatomic details have gained spectacular complexity during the past decades.37,45,46 Today, it must be acknowledged that the structure and function of the basal ganglia are more complicated than described in the aforementioned models. In a seminal review article in this journal, Mega and Cummings47 described five parallel anatomic circuits linking frontal to subcortical regions in the human brain. Each circuit was named according to its cortical site of origin, and their descriptions included not only anatomic details but also their neurochemical modulation and their clinical correlates. In Table 1, we replicate this approach, updating the information as needed. The following paragraph introduces some of the most relevant new information about these frontal-subcortical circuits.
TABLE 1. Main Cortico-Subcortical Circuits Serve Specific Functions and Are Affected by Specific Neurochemical Modulatorsa
Frontal-Subcortical Circuit IdentificationMotor CircuitOculomotor CircuitDorsolateral PrefrontalLateral OrbitofrontalAnterior Cingulate
Pathway ClassificationMotorNonmotor
Anatomical site of originSupplementary motor areaFrontal eye fieldsBrodmann’s areas 9, 10Brodmann’s areas 10, 11Anterior cingulate
Major functionsMotor control, selection of motor plans109,255Oculomotor controlExecutive, cognitive, strategy generation, learning, working memory, verbal skills, pain modulation256,257Personality, social behavior, empathy, prediction of outcome258,259Motivation, decision making, conflict monitoring, attention260,261
Neurochemical modulationDopamine, 5-HT, norepinephrine, acetylcholine, neuroactive peptides80,82,87,173Dopamine, acetylcholine262Dopamine, glucocorticoids, estrogen, norepinephrine263Dopamine, 5-HT264,265Dopamine, 5-HT, μ-opioid, norepinephrine266,267
Associated disordersMovement disorders: Parkinson’s disease, Huntington’s chorea, dystonia, Tourette’s syndrome, essential tremorAbnormal saccades, ophthalmoparesis, nystagmus, gaze anomalies. Can be affected in Parkinson's disease, Huntington's chorea, autism, and Lewy body dementia.268,269Executive dysfunction, reduced verbal fluency, poor organizational strategies, altered learning, depression, anxiety, schizophrenia270,271Disinhibition, irritability, lability, tactlessness, euphoria, unempathic behavior, undue familiarity, impulsivity, OCD, drug addiction, Tourette’s syndrome272274Apathy, akinetic mutism, psychic emptiness, indifference to pain, poverty of spontaneous speech, inattention, emotional instability, OCD, ADHD275277
a
The cortico-basal-ganglia-thalamo-cortical circuits can be divided into two motor pathways (motor and oculomotor) and three nonmotor pathways (dorsolateral prefrontal, anterior cingulate, and lateral orbitofrontal).278 All these circuits project to the basal ganglia through the striatum and back to the cortex through the thalamus.47 Although there is some superposition and cross-talk between subcircuits, cortical topography is maintained from the cortex throughout the whole basal ganglia circuit.279 The neurochemistry of these circuits is rich, and their symptomatology behaves accordingly. Since dopaminergic projections from the substantia nigra pars compacta innervate the entire striatum, dopamine loss (parkinsonism) affects all frontal-subcortical circuits. Some symptoms are specific of certain subcircuits, while others can be related to dysfunction of several subcircuits. Abbreviations: ADHD=attention-deficit hyperactivity disorder; OCD=obsessive-compulsive disorder; 5-HT=serotonin (5-hydroxytryptamine).
The striatal microcircuitry, once considered as a simple structure converging cortical input on deep nuclei, is now viewed as a complex network processing efference from not only the cortex and the basal ganglia but also from extrabasal ganglia origins, and that includes multiple interneuron types.4852 GABAergic interneurons play an important role in the striatum. While a subpopulation of GABAergic interneurons exerts their inhibition on a short distance, another subpopulation bridges distant striatal territories.5355 These newly discovered anatomic details underlie complex processing of motor and cognitive information in this major input center of the basal ganglia circuit.49 Furthermore, the density of gap junctions that interconnect striatal interneurons, also connecting them electrically to MSN, underscores the scale of integration of cortical drive across different functional territories of the striatum.46 In addition, anatomical and pharmaco-biochemical studies have accumulated a large body of evidence showing that glutamatergic, GABAergic, and dopaminergic interactions are insufficient to characterize neuromodulation in the striatum.50,51
The basal ganglia are not an isolated set of nuclei. They receive indolaminergic, catecholaminergic, and acetylcholinergic projections from extrabasal ganglia regions, all of which contribute to modulation of the activity of basal ganglia neurons and interneurons. In the case of Parkinson’s disease, loss of neurons in the pedunculopontine nucleus (PPN),56 the locus coeruleus,5760 and the substantia nigra pars compacta5760 is characteristic and contributes to both motor and neuropsychiatric symptoms.
The dorsal raphe nucleus (DRN) sends serotoninergic projections to the basal ganglia including the striatum; the globus pallidus; the substantia nigra; and, to a lower extent, the STN. Serotoninergic receptors are expressed all along the Cx-BG-Th-Cx circuits, if with some heterogeneity. The decrease in serotonin levels in the basal ganglia is well documented both in postmortem studies on parkinsonian patients61,62 and in animal models.62,63 Clinically, the alteration of serotoninergic neurotransmission contributes to the high incidence of depression in Parkinson’s disease.64
The PPN projects acetylcholinergic fibers diffusely throughout the Cx-BG-Th-Cx circuits (striatum,65,66 STN,6574 substantia nigra mostly pars compacta,65,66,75 globus pallidus65,66,71 including external66,73 and internal segments,66,73 thalamus66,71,73,76,77 including the central medial-parafascicular complex,73,74 and primary motor cortex74), in addition to brainstem nuclei, cerebellum, hypothalamus, and spinal cord.78 While the PPN has been considered mostly involved in the regulation of locomotion and sleep, there is an accumulation of evidence favoring its role in action-reward prediction, learning, attention and decision making.79
The locus coeruleus (A6) projects noradrenergic fibers to the Cx-BG-Th-Cx circuits, including the intralaminar complex of the thalamus and the cortex as well as the brainstem nuclei, cerebellum, hypothalamus, and spinal cord.80 Aberrancy of noradrenergic neurotransmission to these circuits may contribute to depressive symptoms related to parkinsonism, but the modulation of basal ganglia functions by the locus coeruleus remains poorly understood.81
Several neuroactive peptides modulate the Cx-BG-Th-Cx circuits, including tachykinins, enkephalins, dynorphin, somatostatin, and neuropeptide Y, all of which are used by distinct subsets of basal ganglia neurons.82 Moreover, the function and structure of higher cortical areas, such as the insula and other areas of cortex, are also affected by functional and structural abnormalities of the frontal-subcortical circuits, and their secondary dysfunction has the potential to further contribute to neurobehavioral symptoms among persons with disorders affecting the basal ganglia.60,83
The basal ganglia are complex architecturally, neurochemically, and with respect to their structural and functional connectivity. The simplified, traditional structural-functional model of the basal ganglia has provided a useful heuristic for research and education.1 However, it is naïve anatomically and functionally, particularly with respect to its reliance on linear dynamics in explorations of the pathophysiology of basal ganglia disorders. Developing new pharmacological and procedural approaches that more effectively treat basal ganglia disorders requires new heuristics that acknowledges and incorporates more fully the complexity of these structures and the circuits and networks to which they contribute.

Rate-Based Model of the Basal Ganglia

The description of summed excitatory/inhibitory drives onto the output nuclei of the basal ganglia provides a framework for considering firing rate as the coding scheme of neural information in the Cx-BG-Th-Cx circuits. In neuronal systems, a rate code assumes proportionality between the firing rate of neurons and some environmental clue or input signal. If this proportionality holds, the state of the neuronal system can be characterized by the central tendency of the firing rate, defined over a given time window.84,85 A trait of such a rate code is its robustness to noise, since information is carried by the central tendency and not by the precise timing of single spikes.
Evidence in favor of a rate code in the basal ganglia links striatal dopamine function to firing rate and motor activity as well as nonmotor activity, in normal and pathological conditions.8689 In the nonmotor domain, a rate code plays a role in aversion learning and reward encoding in the ventral pallidum, probably accompanied by a population code.90,91 In the sensorimotor system, a rate code is supported by the negative correlation between the rate of discharge of the GPi and specific features of motor activity, like movement onset and velocity.92 In parkinsonism, two main categories of clinical and experimental studies support the rate hypothesis: 1) studies reporting higher firing rate in the GPi of parkinsonian subjects and 2) studies reporting that prodopaminergic therapies reduce the GPi firing rate.9397 Extensive discussion about these studies can be found in the literature.1,98,99
On the other hand, a large body of experimental and clinical evidence contradicts the rate-based model at every level of the Cx-BG-Th-Cx circuit. In the primate GPi, the predicted proportionality between firing rate and severity of parkinsonian symptoms is absent, and hypokinetic symptoms can be observed without any increased firing rate.100 Direct measurements from the human STN also contradict the rate hypothesis: the firing rate in the STN of parkinsonian and epileptic patients without movement disorders is similar.101 In the striatum, MSN show local hypermetabolism and higher firing rate in Parkinson’s disease, but prodopaminergic treatments further increase their firing rate, contradicting the predictions of the rate-based model once again.102,103 At the cortical level, parkinsonian symptoms can occur without change in the mean spontaneous discharge rate of neurons.104 Finally, the rate hypothesis fails to explain the benefits of deep brain stimulation (DBS) across the spectrum of movement disorders.105107 This growing body of controversies around the rate-based model of the basal ganglia has weakened the hypothesis of rate coding in the basal ganglia.108110

Oscillatory Model of the Basal Ganglia

The second main hypothesis of processing of information in the basal ganglia is based on oscillatory phenomena, which can be identified both in single neurons and network activity (local field potentials [LFP]) along the Cx-BG-Th-Cx circuit.100104 Oscillatory activity has been considered a coding scheme in some work, but more often it is regarded as an emergent property.1,115118 In movement disorders, experimental and clinical measurements have reported positive correlations between β-band power (10–30 Hz) and hypokinetic conditions,1,113,119131 as well as positive correlations between γ-band power (over 40 Hz) and motor activity.132136
Regarding parkinsonian symptoms, β-band power in the basal ganglia correlates with tremor,137 rigidity,138 bradykinesia,139 and freezing of gait.140,141 As could be expected, oscillations in the basal ganglia correlate not only with motor function/dysfunction but also with neuropsychiatric symptoms.114 In the α-band (8–12 Hz), oscillations are influenced by emotional stimuli and depend on the affective state, being altered in depressed patients with Parkinson’s disease.142 Impulse control disorders have characteristic oscillations in the theta-alpha band (4–10 Hz) in the STN in patients with Parkinson’s disease.143 Theta and beta power in the STN are also related to decision making and conflict evaluation.144,145 Oscillations in the β-band in the cortex and the basal ganglia are associated with behavioral manifestations, such as behavioral stopping, semantic encoding, and memory.117,146,147 Finally, γ-band activity is related to cognitive processing.148,149
Although oscillatory activity has been identified over a broad spectrum of frequencies in the basal ganglia, the oscillatory hypothesis focuses mostly on two frequency bands, the β-band and the γ-band, resembling the notion of two opposite control subcircuits of the rate-based model: prokinetic and akinetic. However, evidence is contradictory with respect to this hypothesis. The relation between high β-band power and hypokinetic states is challenged by studies comparing LFP in different movement disorders.150153 The proportionality between β-band power and the severity of symptoms remains debated as well.100 In the cortex, findings are also contradictory: The effect of L-DOPA on β-band activity reports either no change, increased activity, decreased activity, or mixed effects.154,155 Concerning the γ-band, although γ-band activity has been reported as stronger in the parkinsonian state, γ-band power can be further increased by antiparkinsonian treatments.155,156
Adaptive mechanisms have been proposed to explain these discrepancies and integrate these studies into the framework of the oscillatory hypothesis.157 In the Cx-BG-Th-Cx loop, the increase in γ-band activity was suggested as a prokinetic adaptive mechanism to oppose the putative antikinetic effects of the pathological increase in β-band activity.156 At a larger scale, the increased connectivity in the cerebello-thalamo-cortical loops is envisaged as a prokinetic mechanism to compensate the dysfunctional Cx-BG-Th-Cx loops.158
The anatomical origin and mechanisms of propagation of oscillatory activity across the frontal-subcortical circuit remain unclear, in health and in Parkinson’s disease.159 Indeed, a unifying hypothesis of the causal relation between oscillatory power at different frequency bands and clinical manifestations of disease remains undeveloped. The challenge to its development is the multiplicity of the phenomena for which it needs to account, including attention processing, periodic sampling of the state of the periphery, increased response inhibition, and mechanisms to maintain the neurobehavioral and motoric status quo.1,119,120,147,160163

Pitfalls of the Rate and Oscillatory Models

The preceding review suggests that the pathophysiology of the basal ganglia is associated with alterations in the neuronal firing rate and oscillatory activity98,110,164,165 but neither model—both of which are predicated on linear measures of single unit activity and/or LFP—provides satisfactory accounts of Cx-BG-Th-Cx circuit function and dysfunction in health and disease. In particular, Parkinson’s disease, the classic basal ganglia disorder, challenges the utility of these models.
Critics of linear dynamic models of the basal ganglia have pointed out that Parkinson’s disease may be understood as a breakdown of information processing in cortico-subcortical circuits with a wide range of motor symptoms (including tremor, rigidity, bradykinesia, and hypokinesia), autonomic dysfunctions (orthostatic hypotension, altered heart rate variability), sleep disturbance, pain, and cognitive and neuropsychiatric manifestations.166 In the sensorimotor domain, neither a change in firing rate nor a change in oscillatory activity can at once explain rigidity, difficulty initiating movement, rhythmic muscle contractions (i.e., tremor), and problems with integrative motor function (i.e., coordination). Similarly, these models also cannot account for the concurrent manifestation of the broad range of nonmotor symptoms—including cognitive, emotional, behavioral, and autonomic symptoms, both hypokinetic and hyperkinetic—produced by Parkinson’s disease. As mentioned by Montgomery,167 the rate and oscillatory hypotheses attempt to account for such symptoms by dichotomizing basal ganglia control mechanisms into two opposing stable states (i.e., “on” and “off”). Therefore, both hypotheses fail for the same reason as explanatory models of basal ganglia pathophysiology: They are unable to account for the complex and inherently mixed nature of parkinsonian symptoms as well as their responses to treatment.167
During the past two decades, new mathematical frameworks have been introduced in the field to characterize the dynamics of the basal ganglia in normal and pathological conditions. These new frameworks show that central tendency measures (e.g., firing rate or frequency power)—and linear features, more generally—cannot fully characterize the dynamics of physiological signals in the Cx-BG-Th-Cx circuits.168170 Conversely, mathematical methods developed in line with Shannon’s171 theory of information allow the quantification of features related to information transmission in complex systems. An analysis of information content in the Cx-BG-Th-Cx circuits might provide new models to describe the whole spectrum of basal ganglia disorders, as opposed to classic models, which force a classification of movement disorders in two categories depending only on the amount of motor activity produced (hypo- and hyperkinetic disorders).167 Additionally, nonlinear analysis methods provide alternative approaches to investigate the effects of treatments that cannot be reduced to either gross excitation or gross inhibition, such as DBS or new therapies targeting the catecholaminergic (i.e., adrenergic) or indolaminergic (e.g., serotoninergic) pathways.87,165,172175
From studies using nonlinear methods of analysis, the temporal structure of spike trains reveals patterns that are characteristic of the activity of healthy and diseased basal ganglia.176 The presence of these patterns implies that: 1) a rate code cannot fully account for the transmission/processing of information in the basal ganglia; and 2) symptoms of Cx-BG-Th-Cx circuit dysfunction are related to a breakdown of temporal organization in the spiking activity of the basal ganglia.85,177

Conceptualization of Basal Ganglia Function Based on Nonlinear Dynamics

The term nonlinear system refers to organized systems generating an output that is not linearly proportional to the system’s input. Nonlinear systems exhibit complex behavior, which cannot be fully described by a linear combination of the individual behavior of their constituent parts.178 While linear dynamic systems are fully predictable (consider the distance that a car travels in 1 hr at 50 km/hour), and stochastic systems are fully unpredictable (consider the lottery), nonlinear systems may exhibit complexity and sensitivity to initial conditions and, hence, limited predictability.179 A historically relevant example in the field of nonlinear dynamics is the weather forecast: while short-term predictions are robust, long-term predictions remain uncertain.180,181 The complex temporal output of nonlinear systems results from the activity of multiple, sometimes parallel or nested, control loops, acting at different time scales.179,181,182
In the basal ganglia, such loops have been identified at the microcircuitry183,184 and macrocircuitry levels.2,86,185,186 The resultant irregularity observed in the signal is not stochastic, but chaotic (see Figure 2). Chaoticity (i.e., the extent to which something is chaotic) can be measured from time series with diverse methods and is directly proportional to the rate of information loss (i.e., entropy) and inversely proportional to predictability.182 While linear methods of analysis focus on central tendencies to describe the state of a system in time and/or frequency domains, nonlinear methods measure the persistence of certain time patterns or “shift” in the irregularity of the time series (related to entropy) and uncover hidden, complex patterns of temporal organization.
FIGURE 2. Entropy Values of Sample Time Seriesa
a Predictability increases from top to bottom. Note the high irregularity of the neuronal signal and the Lorenz attractor. Using a purely statistical approach, these signals can be easily mistaken for a random system, although both correspond to nonlinear systems, in which complex time patterns take place in an organized manner. The panels from top to bottom are as follows: uniform random numbers; neuronal GPi recording of interspike intervals, obtained from a dopamine depleted rat (see Andres et al.242); time evolution for the x variable of the Lorenz attractor (nonlinear dynamical system); and oscillatory signal, y=sin(x). The red bars represent the value of entropy, calculated with the sample entropy method (see Richman and Moorman281): entropy(random numbers)=2.17; entropy(GPi neuron)=1.17, entropy(Lorenz attractor)=0.21; and entropy(sin[x])=0.00.
Normal and pathologic human behaviors are manifestations of the complexity of the nervous system.187,188 This complexity is reflected in brain activity at every scale and can be measured by a variety of methods, including spike trains (i.e., time-series electrical signals) recorded from individual neurons in the brain, electroencephalographic and magnetoencephalographic techniques (EEG and MEG, respectively), and functional magnetic resonance imaging (fMRI).189192 Use of these methods to describe normal complexity in brain activity is essential to understanding and treating changes in the complexity of brain activity wrought by disease, including mental illness.
In a thought-provoking paper, Yang and Tsai193 proposed that mental illness is related to the loss of brain complexity and that it can therefore be measured with complex dynamics’ tools. The foundation for this proposal is evidence of modifications of normal brain complexity in schizophrenia, anxiety disorders, dementias, autistic spectrum conditions, attention deficit/hyperactivity disorder, and sleep disorders,194204 in which contexts, alterations in the complexity of brain activity are related to cognitive and emotional symptoms, may provide biomarkers of disease, and may be remediated by pharmacotherapies.205 In Alzheimer’s disease, cognitive deterioration correlates with a reduction of complexity of brain signals.200,206208 Alterations of the complexity of brain activity are associated with depressive symptoms, including reduced complexity of the EEG in patients with depression,209,210 increased complexity of the MEG in younger patients with depression,195 and increased complexity in the EEG in patients with chronic stress.211 Reduction of MEG-determined complexity correlated with pharmacologically-induced remission of depressive symptoms.195 In summary, a healthy psyche is related to a certain degree of age-dependent complexity in brain activity,212 which can be measured with nonlinear methods from different invasive, as well as noninvasive, recordings of neurophysiologic signals.
In the case of the basal ganglia, a growing body of evidence shows that irregularity in physiological signals is not random in nature but exhibits complex, nonlinear temporal organization in neuronal firing activity,195,213217 LFP/EEG,218223 electromiography224,225 and movement kinetics.226229 Stam et al.230 identified nonlinear features in the EEG recorded from parkinsonian patients.230 Later, entropy of EEG was reported to be higher in parkinsonian patients compared with healthy controls.231,232 This finding was confirmed by Han et al., who further indicated that Parkinson’s disease is associated with increased complexity of the EEG’s rhythm.233 Hohlefeld et al.234 reported a positive correlation between long-range temporal correlations ([LRTC] a measure of temporal organization over different time scales) in EEG recordings and deep LFP from parkinsonian patients, supporting that complex patterns can be repeated across time scales ranging from milliseconds to tens of seconds.234 Further studies have suggested that LRTC is a potential biomarker of pathological processing in basal ganglia disorders.235,236 In Parkinson’s disease, chaoticity in the activity of GPi neurons diminishes as the severity of motor symptoms increases.237 However, since normal brain complexity also diminishes with age, these effects could well depend on age or cognitive decline, a phenomenon that has not been studied yet.238 Using a neural network model, Li and Sikström239 modeled aging-dependent reduction of dopaminergic modulation by reducing neuronal nonlinearity, suggesting a mechanism that could be shared by normal aging and neurodegeneration.239
At the neuronal level, nonlinear temporal organization has been identified in interspike intervals (ISI) recorded from basal ganglia neurons in rodents, primates, and patients with movement disorders.234237,240242 The irregularity of ISI from basal ganglia neurons results from the replication of complex patterns, which cannot be statistically explained from the probability distribution around a central tendency.108,170,177, 243,244 Neurons in the striatum code reward of actions and integrate reward into movement, leading to action selection in specific social situations and participation in reward-based learning.245,246 As is the case with motor symptoms, complex electrophysiological manifestations accompany neurobehavioral manifestations of basal ganglia dysfunction.247,248 In light of the evidence, it is clear that any linear model is inadequate to characterize the irregularity produced by the basal ganglia and the information they convey.169,249 The notion of a complex “neuronal language,” which has emerged from direct observations on the temporal organization of ISI from basal ganglia neurons, can help in building a new conceptual framework to study the pathophysiology of this circuit.85,170,182,237,250

Entropy Hypothesis

The term entropy comes from the field of thermodynamics, where it is related to the number of possible states of a system. Applied to the analysis of spike trains, entropy measures temporal irregularity. Statistical quantities, such as the standard deviation (SD) or the coefficient of variation (CV), measure dispersion of a signal from its central tendency, independently from the temporal order of the data. Something similar happens with frequential analyses, where the temporal domain is lost (e.g., the Fourier spectrum).
By contrast, the order of events in the time series is the crucial factor for the calculation of entropy, as well as other nonlinear measures (temporal structure, LRTC, among others). The value of entropy increases when an observed pattern is not followed by similar patterns in a time series. A time series that is highly predictable has relatively low entropy; a less predictable, more irregular process has higher entropy.
For example, consider the following sequences: A=[2 2 2 1 2 1 1 1 1 2], and B=[1 2 1 2 1 2 1 2 1 2]. Simple statistical measures are equal for both data streams: mean(A)=mean(B)=1.50, SD(A)=SD(B)=0.53, and CV(A)=CV(B)=0.35. However, entropy is higher in the first case; i.e., the more irregular time series (A). The following definitions apply, where x is the independent variable and n is the number of data samples:
Mean: ; SD: ; CV:
Approximate entropy algorithm, m and r fixed at 2 and 0.1, respectively: ApEn(A)=0.51 and ApEn(B)=0.01.
Since linear measures are insufficient to fully characterize the complex nature of neural signals, entropy, among other nonlinear quantities, needs to be incorporated to describe the dynamics of the basal ganglia.170,240,241,243,251,252 Figure 2 presents an example of a time series produced by a random number generator; a GPi neuron; a nonlinear system (Lorenz attractor); and a fully periodic system, sin(x), with their respective entropy values. The irregularity of ISI stands out as a striking feature of neuronal activity in the basal ganglia. This is similar in both normal and pathologic conditions. In the example shown, the entropy of a typical “parkinsonian” GPi neuron is above that measured from the low-dimensional nonlinear system but below the entropy of a stochastic system (a random number generator). As we discussed earlier, while linear models are unable to explain cognitive and behavioral symptomatology of basal ganglia disease, complexity measures show great potential as hallmarks of mental illness. Our proposal is that the deficiencies observed with linear models with respect to the motor, cognitive, emotional, and behavioral manifestations of frontal-subcortical circuit disorders can be addressed with nonlinear techniques. Hyperkinetic and akinetic motor disturbances are related to alterations in the complexity of activity in the frontal-subcortical circuits: neuronal entropy seems to be directly related to akinetic disorders and inversely so to hyperkinetic disorders.250 How to reconcile excesses and deficits of cognition, emotion, and behavior associated with disturbances of these circuits and nonlinear measures is a promising field of research for the next years.
Experimental and clinical investigations show that dopamine modulates neuronal entropy in the basal ganglia. In Sprague-Dawley rats, lesions of the nigrostriatal pathway modify the relation between alertness and entropy at the entopeduncular nucleus (EPN, equivalent to the GPi in primates).242,253 In this species’ EPN, the effect of dopamine depletion is a simultaneous increase of neuronal entropy and firing rate, plus an altered relationship between both.254 Specifically, under nigrostriatal lesion, neurons with high firing rate in the output basal ganglia fail to downregulate entropy, offering a functional hallmark of chronic dopamine depletion.244 Importantly, antiparkinsonian treatment (apomorphine, as well as DBS) reduces basal ganglia entropy.251,252 Therefore, temporal organization of spike trains needs to be investigated as a hidden feature behind the effects of antiparkinsonian drugs. In contrast to the rate and oscillatory hypotheses, the entropy hypothesis supports the idea that irregularity in the time series of ISI is the result of dynamic processing of information within the basal ganglia. The conceptual weight of the model is shifted from anatomical connectivity to information processing.

Conclusions

Integration of the complexity of neuronal activity into new pathophysiological models of the basal ganglia based on information theory and nonlinear dynamics is needed to account for the broad spectrum of motor and neuropsychiatric symptoms associated with frontal-subcortical circuit disorders. We propose that alterations of the temporal organization of spike trains in the basal ganglia imply that neuronal information is transmitted aberrantly and produces alterations of the function of these circuits that manifest as cognitive, emotional, behavioral, motor, and autonomic disturbances. The neuronal language analogy illustrates how the breakdown of communication between subcortical, cortical, and peripheral components of the nervous system results in the abnormal transmission of neuronal information, of which entropy is a global measure, and offers promising new avenues for the study of therapeutics and biomarkers for basal ganglia disorders.

Acknowledgments

The authors thank the staff at the National University of San Martin.

References

1.
Gatev P, Darbin O, Wichmann T: Oscillations in the basal ganglia under normal conditions and in movement disorders. Mov Disord 2006; 21:1566–1577
2.
Middleton FA, Strick PL: Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Res Brain Res Rev 2000; 31:236–250
3.
Brown LL, Schneider JS, Lidsky TI: Sensory and cognitive functions of the basal ganglia. Curr Opin Neurobiol 1997; 7:157–163
4.
Arsalidou M, Duerden EG, Taylor MJ: The centre of the brain: topographical model of motor, cognitive, affective, and somatosensory functions of the basal ganglia. Hum Brain Mapp 2013; 34:3031–3054
5.
Chaudhuri A, Behan PO: Fatigue and basal ganglia. J Neurol Sci 2000; 179(S 1-2):34–42
6.
Tremblay L, Worbe Y, Thobois S, et al: Selective dysfunction of basal ganglia subterritories: From movement to behavioral disorders. Mov Disord 2015; 30:1155–1170
7.
Poletti M, De Rosa A, Bonuccelli U: Affective symptoms and cognitive functions in Parkinson’s disease. J Neurol Sci 2012; 317:97–102
8.
Chacko RC, Corbin MA, Harper RG: Acquired obsessive-compulsive disorder associated with basal ganglia lesions. J Neuropsychiatry Clin Neurosci 2000; 12:269–272
9.
Qiu A, Crocetti D, Adler M, et al: Basal ganglia volume and shape in children with attention deficit hyperactivity disorder. Am J Psychiatry 2009; 166:74–82
10.
Chaudhuri KR, Healy DG, Schapira AHV; National Institute for Clinical Excellence: Non-motor symptoms of Parkinson’s disease: diagnosis and management. Lancet Neurol 2006; 5:235–245
11.
Park A, Stacy M: Non-motor symptoms in Parkinson’s disease. J Neurol 2009; 256(Suppl 3):293–298
12.
Rossi M, Perez-Lloret S, Millar Vernetti P, et al: Olfactory dysfunction evaluation is not affected by comorbid depression in Parkinson’s disease. Mov Disord 2015; 30:1275–1279
13.
Obeso JA, Rodriguez-Oroz MC, Stamelou M, et al: The expanding universe of disorders of the basal ganglia. Lancet 2014; 384:523–531
14.
Cummings JL, Mega M, Gray K, et al: The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology 1994; 44:2308–2314
15.
Aarsland D, Litvan I, Larsen JP: Neuropsychiatric symptoms of patients with progressive supranuclear palsy and Parkinson’s disease. J Neuropsychiatry Clin Neurosci 2001; 13:42–49
16.
Kulisevsky J, Litvan I, Berthier ML, et al: Neuropsychiatric assessment of Gilles de la Tourette patients: comparative study with other hyperkinetic and hypokinetic movement disorders. Mov Disord 2001; 16:1098–1104
17.
Paulsen JS, Ready RE, Hamilton JM, et al: Neuropsychiatric aspects of Huntington’s disease. J Neurol Neurosurg Psychiatry 2001; 71:310–314
18.
Litvan I, Cummings JL, Mega M: Neuropsychiatric features of corticobasal degeneration. J Neurol Neurosurg Psychiatry 1998; 65:717–721
19.
Aarsland D, Brønnick K, Ehrt U, et al: Neuropsychiatric symptoms in patients with Parkinson’s disease and dementia: frequency, profile and associated care giver stress. J Neurol Neurosurg Psychiatry 2007; 78:36–42
20.
Braak H, Del Tredici-Braak K: Evolutional aspects of Alzheimer’s disease pathogenesis. J Alzheimers Dis 2012; 33(Suppl 1):S155–S161
21.
Krause M, Mahant N, Kotschet K, et al: Dysexecutive behaviour following deep brain lesions--a different type of disconnection syndrome? Cortex 2012; 48:97–119
22.
Laplane D, Levasseur M, Pillon B, et al: Obsessive-compulsive and other behavioural changes with bilateral basal ganglia lesions. A neuropsychological, magnetic resonance imaging and positron tomography study Brain 1989; 112:699–725
23.
Jokinen P, Karrasch M, Brück A, et al: Cognitive slowing in Parkinson’s disease is related to frontostriatal dopaminergic dysfunction. J Neurol Sci 2013; 329:23–28
24.
Levy R, Czernecki V: Apathy and the basal ganglia. J Neurol 2006; 253(Suppl 7):VII54–VII61
25.
Levy R, Dubois B: Apathy and the functional anatomy of the prefrontal cortex-basal ganglia circuits. Cereb Cortex 2006; 16:916–928
26.
Rosenblatt A, Leroi I: Neuropsychiatry of Huntington’s disease and other basal ganglia disorders. Psychosomatics 2000; 41:24–30
27.
Monastero R, Di Fiore P, Ventimiglia GD, et al: The neuropsychiatric profile of Parkinson’s disease subjects with and without mild cognitive impairment. J Neural Transm (Vienna) 2013; 120:607–611
28.
Ravina B, Camicioli R, Como PG, et al: The impact of depressive symptoms in early Parkinson disease. Neurology 2007; 69:342–347
29.
Riedel O, Heuser I, Klotsche J, et al: GEPAD Study Group: Occurrence risk and structure of depression in Parkinson disease with and without dementia: results from the GEPAD Study. J Geriatr Psychiatry Neurol 2010; 23:27–34
30.
Burn DJ: Depression in Parkinson’s disease. Eur J Neurol 2002; 9(Suppl 3):44–54
31.
Pontone GM, Williams JR, Anderson KE, et al: Prevalence of anxiety disorders and anxiety subtypes in patients with Parkinson’s disease. Mov Disord 2009; 24:1333–1338
32.
Johnson JM, Legesse B, Camprodon JA, et al: The clinical significance of bilateral basal ganglia calcification presenting with mania and delusions. J Neuropsychiatry Clin Neurosci 2013; 25:68–71
33.
Hwang J, Lyoo IK, Dager SR, et al: Basal ganglia shape alterations in bipolar disorder. Am J Psychiatry 2006; 163:276–285
34.
Menon V, Anagnoson RT, Glover GH, et al: Functional magnetic resonance imaging evidence for disrupted basal ganglia function in schizophrenia. Am J Psychiatry 2001; 158:646–649
35.
Yoon JH, Minzenberg MJ, Raouf S, et al: Impaired prefrontal-basal ganglia functional connectivity and substantia nigra hyperactivity in schizophrenia. Biol Psychiatry 2013; 74:122–129
36.
Alexander GE, Crutcher MD, DeLong MR: Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, “prefrontal” and “limbic” functions. Prog Brain Res 1990; 85:119–146
37.
Kemp JM, Powell TP: The cortico-striate projection in the monkey. Brain 1970; 93:525–546
38.
Nambu A, Tokuno H, Hamada I, et al: Excitatory cortical inputs to pallidal neurons via the subthalamic nucleus in the monkey. J Neurophysiol 2000; 84:289–300
39.
Nambu A: Somatotopic organization of the primate basal ganglia. Front Neuroanat 2011; 5:26
40.
West AR, Floresco SB, Charara A, et al: Electrophysiological interactions between striatal glutamatergic and dopaminergic systems. Ann N Y Acad Sci 2003; 1003:53–74
41.
Gerfen CR, Surmeier DJ: Modulation of striatal projection systems by dopamine. Annu Rev Neurosci 2011; 34:441–466
42.
Nambu A, Tokuno H, Takada M: Functional significance of the cortico-subthalamo-pallidal ‘hyperdirect’ pathway. Neurosci Res 2002; 43:111–117
43.
Darbin O: The aging striatal dopamine function. Parkinsonism Relat Disord 2012; 18:426–432
44.
Nambu A: A new approach to understand the pathophysiology of Parkinson’s disease. J Neurol 2005; 252(Suppl 4):IV1–IV4
45.
DeLong MR, Wichmann T: Basal ganglia circuits as targets for neuromodulation in Parkinson disease. JAMA Neurol 2015; 72:1354–1360
46.
Sesack SR, Grace AA: Cortico-basal ganglia reward network: microcircuitry. Neuropsychopharmacology 2010; 35:27–47
47.
Mega MS, Cummings JL: Frontal-subcortical circuits and neuropsychiatric disorders. J Neuropsychiatry Clin Neurosci 1994; 6:358–370
48.
Marin O, Anderson SA, Rubenstein JL: Origin and molecular specification of striatal interneurons. J Neurosci 2000; 20:6063–6076
49.
Silberberg G, Bolam JP: Local and afferent synaptic pathways in the striatal microcircuitry. Curr Opin Neurobiol 2015; 33:182–187
50.
Mallet N, Schmidt R, Leventhal D, et al: Arkypallidal cells send a stop signal to striatum. Neuron 2016; 89:308–316
51.
Zgaljardic DJ, Foldi NS, Borod JC: Cognitive and behavioral dysfunction in Parkinson’s disease: neurochemical and clinicopathological contributions. J Neural Transm (Vienna) 2004; 111:1287–1301
52.
Yamamoto M: Depression in Parkinson’s disease: its prevalence, diagnosis, and neurochemical background. J Neurol 2001; 248(Suppl 3):III5–III11
53.
Gruen RJ, Friedhoff AJ, Coale A, et al: Tonic inhibition of striatal dopamine transmission: effects of benzodiazepine and GABAA receptor antagonists on extracellular dopamine levels. Brain Res 1992; 599:51–56
54.
Darbin O, Wichmann T: Effects of striatal GABA A-receptor blockade on striatal and cortical activity in monkeys. J Neurophysiol 2008; 99:1294–1305
55.
Melzer S, Gil M, Koser DE, et al: Distinct corticostriatal GABAergic neurons modulate striatal output neurons and motor activity. Cell Reports 2017; 19:1045–1055
56.
Del Tredici K, Braak H: Lewy pathology and neurodegeneration in premotor Parkinson’s disease. Mov Disord 2012; 27:597–607
57.
Halliday GM, Li YW, Blumbergs PC, et al: Neuropathology of immunohistochemically identified brainstem neurons in Parkinson’s disease. Ann Neurol 1990; 27:373–385
58.
German DC, Manaye KF, White CL 3rd, et al: Disease-specific patterns of locus coeruleus cell loss. Ann Neurol 1992; 32:667–676
59.
Mann DM, Yates PO: Pathological basis for neurotransmitter changes in Parkinson’s disease. Neuropathol Appl Neurobiol 1983; 9:3–19
60.
Braak H, Del Tredici K, Rüb U, et al: Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 2003; 24:197–211
61.
Scatton B, Javoy-Agid F, Rouquier L, et al: Reduction of cortical dopamine, noradrenaline, serotonin and their metabolites in Parkinson’s disease. Brain Res 1983; 275:321–328
62.
Politis M, Niccolini F: Serotonin in Parkinson’s disease. Behav Brain Res 2015; 277:136–145
63.
Tadaiesky MT, Dombrowski PA, Figueiredo CP, et al: Emotional, cognitive and neurochemical alterations in a premotor stage model of Parkinson’s disease. Neuroscience 2008; 156:830–840
64.
Cummings JL: Depression and Parkinson’s disease: a review. Am J Psychiatry 1992; 149:443–454
65.
Lavoie B, Parent A: Pedunculopontine nucleus in the squirrel monkey: projections to the basal ganglia as revealed by anterograde tract-tracing methods. J Comp Neurol 1994; 344:210–231
66.
Saper CB, Loewy AD: Projections of the pedunculopontine tegmental nucleus in the rat: evidence for additional extrapyramidal circuitry. Brain Res 1982; 252:367–372
67.
Nomura S, Mizuno N, Sugimoto T: Direct projections from the pedunculopontine tegmental nucleus to the subthalamic nucleus in the cat. Brain Res 1980; 196:223–227
68.
Edley SM, Graybiel AM: The afferent and efferent connections of the feline nucleus tegmenti pedunculopontinus, pars compacta. J Comp Neurol 1983; 217:187–215
69.
Hammond C, Rouzaire-Dubois B, Féger J, et al: Anatomical and electrophysiological studies on the reciprocal projections between the subthalamic nucleus and nucleus tegmenti pedunculopontinus in the rat. Neuroscience 1983; 9:41–52
70.
Bevan MD, Bolam JP: Cholinergic, GABAergic, and glutamate-enriched inputs from the mesopontine tegmentum to the subthalamic nucleus in the rat. J Neurosci 1995; 15:7105–7120
71.
Muthusamy KA, Aravamuthan BR, Kringelbach ML, et al: Connectivity of the human pedunculopontine nucleus region and diffusion tensor imaging in surgical targeting. J Neurosurg 2007; 107:814–820
72.
Kita T, Kita H: Cholinergic and non-cholinergic mesopontine tegmental neurons projecting to the subthalamic nucleus in the rat. Eur J Neurosci 2011; 33:433–443
73.
Scarnati E, Gasbarri A, Campana E, et al: The organization of nucleus tegmenti pedunculopontinus neurons projecting to basal ganglia and thalamus: a retrograde fluorescent double labeling study in the rat. Neurosci Lett 1987; 79:11–16
74.
Sugimoto T, Hattori T: Organization and efferent projections of nucleus tegmenti pedunculopontinus pars compacta with special reference to its cholinergic aspects. Neuroscience 1984; 11:931–946
75.
Lavoie B, Parent A: Pedunculopontine nucleus in the squirrel monkey: cholinergic and glutamatergic projections to the substantia nigra. J Comp Neurol 1994; 344:232–241
76.
Smith Y, Raju DV, Pare JF, et al: The thalamostriatal system: a highly specific network of the basal ganglia circuitry. Trends Neurosci 2004; 27:520–527
77.
Smith Y, Paré D, Deschenes M, et al: Cholinergic and non-cholinergic projections from the upper brainstem core to the visual thalamus in the cat. Exp Brain Res 1988; 70:166–180
78.
Martinez-Gonzalez C, Bolam JP, Mena-Segovia J: Topographical organization of the pedunculopontine nucleus. Front Neuroanat 2011; 5:22
79.
Gut NK, Winn P: The pedunculopontine tegmental nucleus-A functional hypothesis from the comparative literature. Mov Disord 2016; 31:615–624
80.
Fornai F, di Poggio AB, Pellegrini A, et al: Noradrenaline in Parkinson’s disease: from disease progression to current therapeutics. Curr Med Chem 2007; 14:2330–2334
81.
Gunaydin LA, Kreitzer AC: Cortico-basal ganglia circuit function in psychiatric disease. Annu Rev Physiol 2016; 78:327–350
82.
Parent A, Côté P-Y, Lavoie B: Chemical anatomy of primate basal ganglia. Prog Neurobiol 1995; 46:131–197
83.
Bertrand E, Lechowicz W, Szpak GM, et al: Limbic neuropathology in idiopathic Parkinson’s disease with concomitant dementia. Folia Neuropathol 2004; 42:141–150
84.
Panzeri S, Brunel N, Logothetis NK, et al: Sensory neural codes using multiplexed temporal scales. Trends Neurosci 2010; 33:111–120
85.
Andres DS: The language of neurons: theory and applications of a quantitative analysis of the neural code. Int J Med Biol Front 2015; 21:133–148
86.
Albin RL, Young AB, Penney JB: The functional anatomy of basal ganglia disorders. Trends Neurosci 1989; 12:366–375
87.
Parent M, Wallman M-J, Gagnon D, et al: Serotonin innervation of basal ganglia in monkeys and humans. J Chem Neuroanat 2011; 41:256–265
88.
Kravitz AV, Tye LD, Kreitzer AC: Distinct roles for direct and indirect pathway striatal neurons in reinforcement. Nat Neurosci 2012; 15:816–818
89.
Freeze BS, Kravitz AV, Hammack N, et al: Control of basal ganglia output by direct and indirect pathway projection neurons. J Neurosci 2013; 33:18531–18539
90.
Itoga CA, Berridge KC, Aldridge JW: Ventral pallidal coding of a learned taste aversion. Behav Brain Res 2016; 300:175–183
91.
Tindell AJ, Berridge KC, Aldridge JW: Ventral pallidal representation of pavlovian cues and reward: population and rate codes. J Neurosci 2004; 24:1058–1069
92.
Utter AA, Basso MA: The basal ganglia: an overview of circuits and function. Neurosci Biobehav Rev 2008; 32:333–342
93.
Filion M, Tremblay L, Bédard PJ: Effects of dopamine agonists on the spontaneous activity of globus pallidus neurons in monkeys with MPTP-induced parkinsonism. Brain Res 1991; 547:152–161
94.
Hutchinson WD, Levy R, Dostrovsky JO, et al: Effects of apomorphine on globus pallidus neurons in parkinsonian patients. Ann Neurol 1997; 42:767–775
95.
Merello M, Balej J, Delfino M, et al: Apomorphine induces changes in GPi spontaneous outflow in patients with Parkinson’s disease. Mov Disord 1999; 14:45–49
96.
Frank MJ, Seeberger LC, O’Reilly RC: By carrot or by stick: cognitive reinforcement learning in parkinsonism. Science 2004; 306:1940–1943
97.
Filion M, Tremblay L: Abnormal spontaneous activity of globus pallidus neurons in monkeys with MPTP-induced parkinsonism. Brain Res 1991; 547:142–151
98.
Nambu A: Seven problems on the basal ganglia. Curr Opin Neurobiol 2008; 18:595–604
99.
Nelson AB, Kreitzer AC: Reassessing models of basal ganglia function and dysfunction. Annu Rev Neurosci 2014; 37:117–135
100.
Muralidharan A, Jensen AL, Connolly A, et al: Physiological changes in the pallidum in a progressive model of Parkinson’s disease: Are oscillations enough? Exp Neurol 2016; 279:187–196
101.
Montgomery EB Jr: Subthalamic nucleus neuronal activity in Parkinson’s disease and epilepsy subjects. Parkinsonism Relat Disord 2008; 14:120–125
102.
Singh A, Liang L, Kaneoke Y, et al: Dopamine regulates distinctively the activity patterns of striatal output neurons in advanced parkinsonian primates. J Neurophysiol 2015; 113:1533–1544
103.
Singh A, Mewes K, Gross RE, et al: Human striatal recordings reveal abnormal discharge of projection neurons in Parkinson’s disease. Proc Natl Acad Sci USA 2016; 113:9629–9634
104.
Goldberg JA, Boraud T, Maraton S, et al: Enhanced synchrony among primary motor cortex neurons in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine primate model of Parkinson’s disease. J Neurosci 2002; 22:4639–4653
105.
Galati S, Mazzone P, Fedele E, et al: Biochemical and electrophysiological changes of substantia nigra pars reticulata driven by subthalamic stimulation in patients with Parkinson’s disease. Eur J Neurosci 2006; 23:2923–2928
106.
Stefani A, Fedele E, Vitek J, et al: The clinical efficacy of L-DOPA and STN-DBS share a common marker: reduced GABA content in the motor thalamus. Cell Death Dis 2011; 2:e154
107.
Stefani A, Fedele E, Pierantozzi M, et al: Reduced GABA content in the motor thalamus during effective deep brain stimulation of the subthalamic nucleus. Front Syst Neurosci 2011; 5:17
108.
Obeso JA, Rodríguez-Oroz MC, Rodríguez M, et al: Pathophysiology of the basal ganglia in Parkinson’s disease. Trends Neurosci 2000; 23(Suppl):S8–S19
109.
Yelnik J: Functional anatomy of the basal ganglia. Mov Disord 2002; 17(Suppl 3):S15–S21
110.
Montgomery EB Jr: Basal ganglia physiology and pathophysiology: a reappraisal. Parkinsonism Relat Disord 2007; 13:455–465
111.
Eusebio A, Brown P: Oscillatory activity in the basal ganglia. Parkinsonism Relat Disord 2007; 13(Suppl 3):S434–S436
112.
Weinberger M, Dostrovsky JO: A basis for the pathological oscillations in basal ganglia: the crucial role of dopamine. Neuroreport 2011; 22:151–156
113.
Stein E, Bar-Gad I: β oscillations in the cortico-basal ganglia loop during parkinsonism. Exp Neurol 2013; 245:52–59
114.
Brittain J-S, Brown P: Oscillations and the basal ganglia: motor control and beyond. Neuroimage 2014; 85:637–647
115.
Sannita WG: Stimulus-specific oscillatory responses of the brain: a time/frequency-related coding process. Clin Neurophysiol 2000; 111:565–583
116.
Meck WH, Penney TB, Pouthas V: Cortico-striatal representation of time in animals and humans. Curr Opin Neurobiol 2008; 18:145–152
117.
Hanslmayr S, Spitzer B, Bäuml K-H: Brain oscillations dissociate between semantic and nonsemantic encoding of episodic memories. Cereb Cortex 2009; 19:1631–1640
118.
Igarashi J, Isomura Y, Arai K, et al: A θ-γ oscillation code for neuronal coordination during motor behavior. J Neurosci 2013; 33:18515–18530
119.
Weinberger M, Hutchison WD, Dostrovsky JO: Pathological subthalamic nucleus oscillations in PD: can they be the cause of bradykinesia and akinesia? Exp Neurol 2009; 219:58–61
120.
Eusebio A, Brown P: Synchronisation in the beta frequency-band--the bad boy of parkinsonism or an innocent bystander? Exp Neurol 2009; 217:1–3
121.
Levy R, Hutchison WD, Lozano AM, et al: High-frequency synchronization of neuronal activity in the subthalamic nucleus of parkinsonian patients with limb tremor. J Neurosci 2000; 20:7766–7775
122.
Bevan MD, Magill PJ, Terman D, et al: Move to the rhythm: oscillations in the subthalamic nucleus-external globus pallidus network. Trends Neurosci 2002; 25:525–531
123.
Nambu A: A new dynamic model of the cortico-basal ganglia loop. Prog Brain Res 2004; 143:461–466
124.
Kühn AA, Kupsch A, Schneider GH, et al: Reduction in subthalamic 8-35 Hz oscillatory activity correlates with clinical improvement in Parkinson’s disease. Eur J Neurosci 2006; 23:1956–1960
125.
Weinberger M, Mahant N, Hutchison WD, et al: Beta oscillatory activity in the subthalamic nucleus and its relation to dopaminergic response in Parkinson’s disease. J Neurophysiol 2006; 96:3248–3256
126.
Tang JK, Moro E, Mahant N, et al: Neuronal firing rates and patterns in the globus pallidus internus of patients with cervical dystonia differ from those with Parkinson’s disease. J Neurophysiol 2007; 98:720–729
127.
Hammond C, Bergman H, Brown P: Pathological synchronization in Parkinson’s disease: networks, models and treatments. Trends Neurosci 2007; 30:357–364
128.
Galvan A, Wichmann T: Pathophysiology of parkinsonism. Clin Neurophysiol 2008; 119:1459–1474
129.
Obeso JA, Marin C, Rodriguez-Oroz C, et al: The basal ganglia in Parkinson’s disease: current concepts and unexplained observations. Ann Neurol 2008; 64(Suppl 2):S30–S46
130.
Kühn AA, Kempf F, Brücke C, et al: High-frequency stimulation of the subthalamic nucleus suppresses oscillatory beta activity in patients with Parkinson’s disease in parallel with improvement in motor performance. J Neurosci 2008; 28:6165–6173
131.
Eusebio A, Thevathasan W, Doyle Gaynor L, et al: Deep brain stimulation can suppress pathological synchronisation in parkinsonian patients. J Neurol Neurosurg Psychiatry 2011; 82:569–573
132.
Jenkinson N, Kühn AA, Brown P: γ oscillations in the human basal ganglia. Exp Neurol 2013; 245:72–76
133.
Dostrovsky J, Bergman H: Oscillatory activity in the basal ganglia--relationship to normal physiology and pathophysiology. Brain 2004; 127:721–722
134.
Fogelson N, Williams D, Tijssen M, et al: Different functional loops between cerebral cortex and the subthalmic area in Parkinson’s disease. Cereb Cortex 2006; 16:64–75
135.
Gale JT, Amirnovin R, Williams ZM, et al: From symphony to cacophony: pathophysiology of the human basal ganglia in Parkinson disease. Neurosci Biobehav Rev 2008; 32:378–387
136.
Weinberger M, Hutchison WD, Lozano AM, et al: Increased gamma oscillatory activity in the subthalamic nucleus during tremor in Parkinson’s disease patients. J Neurophysiol 2009; 101:789–802
137.
Wang S-Y, Aziz TZ, Stein JF, et al: Time-frequency analysis of transient neuromuscular events: dynamic changes in activity of the subthalamic nucleus and forearm muscles related to the intermittent resting tremor. J Neurosci Methods 2005; 145:151–158
138.
Little S, Pogosyan A, Kuhn AA, et al: β band stability over time correlates with Parkinsonian rigidity and bradykinesia. Exp Neurol 2012; 236:383–388
139.
Ray NJ, Jenkinson N, Wang S, et al: Local field potential beta activity in the subthalamic nucleus of patients with Parkinson’s disease is associated with improvements in bradykinesia after dopamine and deep brain stimulation. Exp Neurol 2008; 213:108–113
140.
Singh A, Plate A, Kammermeier S, et al: Freezing of gait-related oscillatory activity in the human subthalamic nucleus. Basal Ganglia 2013; 3:25–32
141.
Toledo JB, López-Azcárate J, Garcia-Garcia D, et al: High beta activity in the subthalamic nucleus and freezing of gait in Parkinson’s disease. Neurobiol Dis 2014; 64:60–65
142.
Huebl J, Schoenecker T, Siegert S, et al: Modulation of subthalamic alpha activity to emotional stimuli correlates with depressive symptoms in Parkinson’s disease. Mov Disord 2011; 26:477–483
143.
Rodriguez-Oroz MC, López-Azcárate J, Garcia-Garcia D, et al: Involvement of the subthalamic nucleus in impulse control disorders associated with Parkinson’s disease. Brain 2011; 134:36–49
144.
Cavanagh JF, Wiecki TV, Cohen MX, et al: Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nat Neurosci 2011; 14:1462–1467
145.
Fumagalli M, Giannicola G, Rosa M, et al: Conflict-dependent dynamic of subthalamic nucleus oscillations during moral decisions. Soc Neurosci 2011; 6:243–256
146.
Swann N, Tandon N, Canolty R, et al: Intracranial EEG reveals a time- and frequency-specific role for the right inferior frontal gyrus and primary motor cortex in stopping initiated responses. J Neurosci 2009; 29:12675–12685
147.
Ray NJ, Brittain J-S, Holland P, et al: The role of the subthalamic nucleus in response inhibition: evidence from local field potential recordings in the human subthalamic nucleus. Neuroimage 2012; 60:271–278
148.
Anzak A, Gaynor L, Beigi M, et al: A gamma band specific role of the subthalamic nucleus in switching during verbal fluency tasks in Parkinson’s disease. Exp Neurol 2011; 232:136–142
149.
Anzak A, Gaynor L, Beigi M, et al: Subthalamic nucleus gamma oscillations mediate a switch from automatic to controlled processing: a study of random number generation in Parkinson’s disease. Neuroimage 2013; 64:284–289
150.
Steigerwald F, Pötter M, Herzog J, et al: Neuronal activity of the human subthalamic nucleus in the parkinsonian and nonparkinsonian state. J Neurophysiol 2008; 100:2515–2524
151.
Silberstein P, Kühn AA, Kupsch A, et al: Patterning of globus pallidus local field potentials differs between Parkinson’s disease and dystonia. Brain 2003; 126:2597–2608
152.
Weinberger M, Hutchison WD, Alavi M, et al: Oscillatory activity in the globus pallidus internus: comparison between Parkinson’s disease and dystonia. Clin Neurophysiol 2012; 123:358–368
153.
Starr PA, Rau GM, Davis V, et al: Spontaneous pallidal neuronal activity in human dystonia: comparison with Parkinson’s disease and normal macaque. J Neurophysiol 2005; 93:3165–3176
154.
George JS, Strunk J, Mak-McCully R, et al: Dopaminergic therapy in Parkinson’s disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control. Neuroimage Clin 2013; 3:261–270
155.
Dupre KB, Cruz AV, McCoy AJ, et al: Effects of L-dopa priming on cortical high beta and high gamma oscillatory activity in a rodent model of Parkinson’s disease. Neurobiol Dis 2016; 86:1–15
156.
Florin E, Erasmi R, Reck C, et al: Does increased gamma activity in patients suffering from Parkinson’s disease counteract the movement inhibiting beta activity? Neuroscience 2013; 237:42–50
157.
Appel-Cresswell S, de la Fuente-Fernandez R, Galley S, et al: Imaging of compensatory mechanisms in Parkinson’s disease. Curr Opin Neurol 2010; 23:407–412
158.
Palmer SJ, Li J, Wang ZJ, et al: Joint amplitude and connectivity compensatory mechanisms in Parkinson’s disease. Neuroscience 2010; 166:1110–1118
159.
Alonso-Frech F, Zamarbide I, Alegre M, et al: Slow oscillatory activity and levodopa-induced dyskinesias in Parkinson’s disease. Brain 2006; 129:1748–1757
160.
Engel AK, Fries P: Beta-band oscillations--signalling the status quo? Curr Opin Neurobiol 2010; 20:156–165
161.
Rivlin-Etzion M, Marmor O, Heimer G, et al: Basal ganglia oscillations and pathophysiology of movement disorders. Curr Opin Neurobiol 2006; 16:629–637
162.
Khanna P, Carmena JM: Neural oscillations: beta band activity across motor networks. Curr Opin Neurobiol 2015; 32:60–67
163.
Mackay WA: Synchronized neuronal oscillations and their role in motor processes. Trends Cogn Sci 1997; 1:176–183
164.
Brown P: Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson’s disease. Mov Disord 2003; 18:357–363
165.
Nambu A, Tachibana Y, Chiken S: Cause of parkinsonian symptoms: firing rate, firing pattern or dynamic activity changes? Basal Ganglia 2015; 5:1–6
166.
Todorova A, Jenner P, Ray Chaudhuri K: Non-motor Parkinson’s: integral to motor Parkinson’s, yet often neglected. Pract Neurol 2014; 14:310–322
167.
Montgomery EB Jr: Modeling and theories of pathophysiology and physiology of the basal ganglia-thalamic-cortical system: critical analysis. Front Hum Neurosci 2016; 10:469
168.
Darbin O, Adams E, Martino A, et al: Non-linear dynamics in parkinsonism. Front Neurol 2013; 4:211
169.
Andres DS, Cerquetti D, Merello M: Multiplexed coding in the human basal ganglia. J Phys Conf Ser 2016; 705:012049
170.
Darbin O, Soares J, Wichmann T: Nonlinear analysis of discharge patterns in monkey basal ganglia. Brain Res 2006; 1118:84–93
171.
Shannon CE: A mathematical theory of communication. Mob Comput Commun Rev 2001; 5:3–55
172.
Darbin O, Risso JJ, Rostain JC: Dopaminergic control of striatal 5-HT level at normobaric condition and at pressure. Undersea Hyperb. Med. 2010; 37:159–166
173.
Mathur BN, Lovinger DM: Serotonergic action on dorsal striatal function. Parkinsonism Relat Disord 2012; 18(Suppl 1):S129–S131
174.
Montgomery EB Jr: One view of the current state of understanding in basal ganglia pathophysiology and what is needed for the future. J Mov Disord 2011; 4:13–20
175.
Chiken S, Nambu A: Disrupting neuronal transmission: mechanism of DBS? Front Syst Neurosci 2014; 8:33
176.
Andres DS, Gomez F, Ferrari FA, et al: Multiple-time-scale framework for understanding the progression of Parkinson’s disease. Phys Rev E Stat Nonlin Soft Matter Phys 2014; 90:062709
177.
Andres DS, Cerquetti D, Merello M: Neural code alterations and abnormal time patterns in Parkinson’s disease. J Neural Eng 2015; 12:026004
178.
Alligod KT, Sauer TD, Yorke, JA: Chaos: An Introduction to Dynamical Systems. New York, Springer, 1996
179.
Schreiber T: Interdisciplinary application of nonlinear time series methods. Phys Rep 1999; 308:64
180.
Lorenz EN: Deterministic nonperiodic flow. J Atmos Sci 1963; 20:130–141
181.
Strogatz SH: Nonlinear Dynamics and Chaos. Cambridge, Perseus, 1994
182.
Hegger R, Kantz H, Schreiber T: Practical implementation of nonlinear time series methods: The TISEAN package. Chaos 1999; 9:413–435
183.
Gittis AH, Kreitzer AC: Striatal microcircuitry and movement disorders. Trends Neurosci 2012; 35:557–564
184.
Calabresi P, Picconi B, Tozzi A, et al: Direct and indirect pathways of basal ganglia: a critical reappraisal. Nat Neurosci 2014; 17:1022–1030
185.
Takada M, Hoshi E, Saga Y, et al: Organization of two cortico–basal ganglia loop circuits that arise from distinct sectors of the monkey dorsal premotor cortex, in Basal Ganglia: An Integrative View. Edited by Barrios FA, Bauer C. InTech, 2012, pp 103
186.
Alexander GE, DeLong MR, Strick PL: Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 1986; 9:357–381
187.
Yang AC, Tsai SJ: Complexity of mental illness: a new research dimension. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:251–252
188.
Bullmore E, Sporns O: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009; 10:186–198
189.
Ibáñez-Molina AJ, Iglesias-Parro S, Soriano MF, et al: Multiscale Lempel-Ziv complexity for EEG measures. Clin Neurophysiol 2015; 126:541–548
190.
Brookes MJ, Hall EL, Robson SE, et al: Complexity measures in magnetoencephalography: measuring “disorder” in schizophrenia. PLoS One 2015; 10:e0120991
191.
Rubin D, Fekete T, Mujica-Parodi LR: Optimizing complexity measures for FMRI data: algorithm, artifact, and sensitivity. PLoS One 2013; 8:e63448
192.
Fetterhoff D, Kraft RA, Sandler RA, et al: Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences. Front Syst Neurosci 2015; 9:130
193.
Yang AC, Tsai SJ: Is mental illness complex? From behavior to brain. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:253–257
194.
Fernández A, López-Ibor MI, Turrero A, et al: Lempel-Ziv complexity in schizophrenia: a MEG study. Clin Neurophysiol 2011; 122:2227–2235
195.
Méndez MA, Zuluaga P, Hornero R, et al: Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment. J Psychopharmacol 2012; 26:636–643
196.
Takahashi T: Complexity of spontaneous brain activity in mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:258–266
197.
Lai M-C, Lombardo MV, Chakrabarti B, et al: MRC AIMS Consortium: A shift to randomness of brain oscillations in people with autism. Biol Psychiatry 2010; 68:1092–1099
198.
Fernández A, Gómez C, Hornero R, et al: Complexity and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:267–276
199.
Chae J-H, Jeong J, Peterson BS, et al: Dimensional complexity of the EEG in patients with posttraumatic stress disorder. Psychiatry Res 2004; 131:79–89
200.
Mizuno T, Takahashi T, Cho RY, et al: Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clin Neurophysiol 2010; 121:1438–1446
201.
Fernández A, Quintero J, Hornero R, et al: Complexity analysis of spontaneous brain activity in attention-deficit/hyperactivity disorder: diagnostic implications. Biol Psychiatry 2009; 65:571–577
202.
Yang AC, Tsai S-J, Yang C-H, et al: Reduced physiologic complexity is associated with poor sleep in patients with major depression and primary insomnia. J Affect Disord 2011; 131:179–185
203.
Catarino A, Churches O, Baron-Cohen S, et al: Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clin Neurophysiol 2011; 122:2375–2383
204.
Sohn H, Kim I, Lee W, et al: Linear and non-linear EEG analysis of adolescents with attention-deficit/hyperactivity disorder during a cognitive task. Clin Neurophysiol 2010; 121:1863–1870
205.
Takahashi T, Cho RY, Mizuno T, et al: Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 2010; 51:173–182
206.
Yang AC, Wang SJ, Lai KL, et al: Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer’s disease. Prog Neuropsychopharmacol Biol Psychiatry 2013; 47:52–61
207.
Jeong J: EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 2004; 115:1490–1505
208.
Jelles B, Scheltens P, van der Flier WM, et al: Global dynamical analysis of the EEG in Alzheimer’s disease: frequency-specific changes of functional interactions. Clin Neurophysiol 2008; 119:837–841
209.
Nandrino J-L, Pezard L, Martinerie J, et al: Decrease of complexity in EEG as a symptom of depression. Neuroreport 1994; 5:528–530
210.
Zhang Y, Wang C, Sun C, et al: Neural complexity in patients with poststroke depression: A resting EEG study. J Affect Disord 2015; 188:310–318
211.
Peng H, Hu B, Zheng F, et al: A method of identifying chronic stress by EEG. Pers Ubiquitous Comput 2013; 17:1341–1347
212.
Iglesias-Parro S, Soriano MF, Ibáñez-Molina AJ: Fractals in Affective and Anxiety Disorders, in The Fractal Geometry of the Brain. Edited by Di Ieva A. New York, Springer, 2016, pp 471–483
213.
Mpitsos GJ, Burton RM Jr, Creech HC, et al: Evidence for chaos in spike trains of neurons that generate rhythmic motor patterns. Brain Res Bull 1988; 21:529–538
214.
Korn H, Faure P: Is there chaos in the brain? II. Experimental evidence and related models. C R Biol 2003; 326:787–840
215.
London M, Häusser M: Dendritic computation. Annu Rev Neurosci 2005; 28:503–532
216.
Friedman A, Deri I, Friedman Y, et al: Decoding of dopaminergic mesolimbic activity and depressive behavior. J Mol Neurosci 2007; 32:72–79
217.
Silver RA: Neuronal arithmetic. Nat Rev Neurosci 2010; 11:474–489
218.
McKenna TM, McMullen TA, Shlesinger MF: The brain as a dynamic physical system. Neuroscience 1994; 60:587–605
219.
Elger CE, Widman G, Andrzejak R, et al: Nonlinear EEG analysis and its potential role in epileptology. Epilepsia 2000; 41(Suppl 3):S34–S38
220.
Manyakov NV, Van Hulle MM: Synchronization in monkey visual cortex analyzed with an information-theoretic measure. Chaos 2008; 18:037130
221.
Kozma R, Bressler S, Perlovsky L, et al: Advances in neural networks research: an introduction. Neural Netw 2009; 22:489–490
222.
Uşakli AB: Modeling of movement-related potentials using a fractal approach. J Comput Neurosci 2010; 28:595–603
223.
Battaglia D, Hansel D: Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex. PLOS Comput Biol 2011; 7:e1002176
224.
Rissanen SM, Kankaanpää M, Meigal A, et al: Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis. Med Biol Eng Comput 2008; 46:849–858
225.
Zhang X, Zhou P: Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes. J Electromyogr Kinesiol 2012; 22:901–907
226.
Buzzi UH, Stergiou N, Kurz MJ, et al: Nonlinear dynamics indicates aging affects variability during gait. Clin Biomech (Bristol, Avon) 2003; 18:435–443
227.
West BJ, Scafetta N: Nonlinear dynamical model of human gait. Phys Rev E Stat Nonlin Soft Matter Phys 2003; 67:051917
228.
Miller DJ, Stergiou N, Kurz MJ: An improved surrogate method for detecting the presence of chaos in gait. J Biomech 2006; 39:2873–2876
229.
Meigal AY, Rissanen SM, Tarvainen MP, et al: Linear and nonlinear tremor acceleration characteristics in patients with Parkinson’s disease. Physiol Meas 2012; 33:395–412
230.
Stam CJ, Jelles B, Achtereekte HA, et al: Investigation of EEG non-linearity in dementia and Parkinson’s disease. Electroencephalogr Clin Neurophysiol 1995; 95:309–317
231.
Jelles B, Achtereekte HA, Slaets JP, et al: Specific patterns of cortical dysfunction in dementia and Parkinson’s disease demonstrated by the acceleration spectrum entropy of the EEG. Clin Electroencephalogr 1995; 26:188–192
232.
Pezard L, Jech R, Růzicka E: Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson’s disease. Clin Neurophysiol 2001; 112:38–45
233.
Han CX, Wang J, Yi GS, et al: Investigation of EEG abnormalities in the early stage of Parkinson’s disease. Cogn Neurodyn 2013; 7:351–359
234.
Hohlefeld FU, Ehlen F, Tiedt HO, et al: Correlation between cortical and subcortical neural dynamics on multiple time scales in Parkinson’s disease. Neuroscience 2015; 298:145–160
235.
Hohlefeld FU, Huebl J, Huchzermeyer C, et al: Long-range temporal correlations in the subthalamic nucleus of patients with Parkinson’s disease. Eur J Neurosci 2012; 36:2812–2821
236.
Hohlefeld FU, Ehlen F, Krugel LK, et al: Modulation of cortical neural dynamics during thalamic deep brain stimulation in patients with essential tremor. Neuroreport 2013; 24:751–756
237.
Andres DS, Cerquetti D, Merello M: Finite dimensional structure of the GPI discharge in patients with Parkinson’s disease. Int J Neural Syst 2011; 21:175–186
238.
Takahashi T, Cho RY, Murata T, et al: Age-related variation in EEG complexity to photic stimulation: A multiscale entropy analysis. Clin Neurophysiol 2009; 120:476–483
239.
Li S-C, Sikström S: Integrative neurocomputational perspectives on cognitive aging, neuromodulation, and representation. Neurosci Biobehav Rev 2002; 26:795–808
240.
Li W, Jia D, Wang JL, et al: Deterministic dynamics in neuronal discharge from pallidotomy targets. J Int Med Res 2008; 36:979–985
241.
Lim J, Sanghera MK, Darbin O, et al: Nonlinear temporal organization of neuronal discharge in the basal ganglia of Parkinson’s disease patients. Exp Neurol 2010; 224:542–544
242.
Andres DS, Cerquetti D, Merello M, et al: Neuronal entropy depends on the level of alertness in the parkinsonian globus pallidus in vivo. Front Neurol 2014; 5:96
243.
Rodríguez M, Pereda E, González J, et al: Neuronal activity in the substantia nigra in the anaesthetized rat has fractal characteristics. Evidence for firing-code patterns in the basal ganglia. Exp Brain Res 2003; 151:167–172
244.
Darbin O, Dees D, Martino A, et al: An entropy-based model for basal ganglia dysfunctions in movement disorders. BioMed research international 2013; 2013:742671
245.
Schultz W: Reward functions of the basal ganglia. J Neural Transm (Vienna) 2016; 123:679–693
246.
Schultz W: Dopamine reward prediction-error signalling: a two-component response. Nat Rev Neurosci 2016; 17:183–195
247.
Moretti DV, Paternicò D, Binetti G, et al: EEG markers are associated to gray matter changes in thalamus and basal ganglia in subjects with mild cognitive impairment. Neuroimage 2012; 60:489–496
248.
Marceglia S, Fiorio M, Foffani G, et al: Modulation of beta oscillations in the subthalamic area during action observation in Parkinson’s disease. Neuroscience 2009; 161:1027–1036
249.
Ferster D, Spruston N: Cracking the neuronal code. Science 1995; 270:756–757
250.
Alam M, Sanghera MK, Schwabe K, et al: Globus pallidus internus neuronal activity: a comparative study of linear and non-linear features in patients with dystonia or Parkinson’s disease. J Neural Transm (Vienna) 2016; 123:231–240
251.
Dorval AD, Russo GS, Hashimoto T, et al: Deep brain stimulation reduces neuronal entropy in the MPTP-primate model of Parkinson’s disease. J Neurophysiol 2008; 100:2807–2818
252.
Lafreniere-Roula M, Darbin O, Hutchison WD, et al: Apomorphine reduces subthalamic neuronal entropy in parkinsonian patients. Exp Neurol 2010; 225:455–458
253.
Nanni F, Andres DS: Structure function revisited: a simple tool for complex analysis of neuronal activity. Front Hum Neurosci 2017; 11:409
254.
Darbin O, Jin X, Von Wrangel C, et al: Neuronal entropy-rate feature of entopeduncular nucleus in rat model of Parkinson’s disease. Int J Neural Syst 2016; 26:1550038
255.
Wichmann T, DeLong MR: Functional and pathophysiological models of the basal ganglia. Curr Opin Neurobiol 1996; 6:751–758
256.
Yoon JH, Grandelis A, Maddock RJ: Dorsolateral prefrontal cortex GABA concentration in humans predicts working memory load processing capacity. J Neurosci 2016; 36:11788–11794
257.
Lorenz J, Minoshima S, Casey KL: Keeping pain out of mind: the role of the dorsolateral prefrontal cortex in pain modulation. Brain 2003; 126:1079–1091
258.
Schoenbaum G, Roesch MR, Stalnaker TA, et al: A new perspective on the role of the orbitofrontal cortex in adaptive behaviour. Nat Rev Neurosci 2009; 10:885–892
259.
Rolls ET: The functions of the orbitofrontal cortex. Brain Cogn 2004; 55:11–29
260.
Botvinick MM: Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cogn Affect Behav Neurosci 2007; 7:356–366
261.
Fallgatter AJ, Bartsch AJ, Herrmann MJ: Electrophysiological measurements of anterior cingulate function. J Neural Transm (Vienna) 2002; 109:977–988
262.
Naicker P, Anoopkumar-Dukie S, Grant GD, et al: Medications influencing central cholinergic neurotransmission affect saccadic and smooth pursuit eye movements in healthy young adults. Psychopharmacology (Berl) 2017; 234:63–71
263.
Shansky RM, Lipps J: Stress-induced cognitive dysfunction: hormone-neurotransmitter interactions in the prefrontal cortex. Front Hum Neurosci 2013; 7:123
264.
Rolls ET: The roles of the orbitofrontal cortex via the habenula in non-reward and depression, and in the responses of serotonin and dopamine neurons. Neurosci Biobehav Rev 2017; 75:331–334
265.
Winstanley CA, Theobald DE, Dalley JW, et al: Double dissociation between serotonergic and dopaminergic modulation of medial prefrontal and orbitofrontal cortex during a test of impulsive choice. Cereb Cortex 2006; 16:106–114
266.
Zubieta JK, Ketter TA, Bueller JA, et al: Regulation of human affective responses by anterior cingulate and limbic mu-opioid neurotransmission. Arch Gen Psychiatry 2003; 60:1145–1153
267.
Lacroix LP, Hows ME, Shah AJ, et al: Selective antagonism at dopamine D3 receptors enhances monoaminergic and cholinergic neurotransmission in the rat anterior cingulate cortex. Neuropsychopharmacology 2003; 28:839-849
268.
Sharma R, Hicks S, Berna CM, et al: Oculomotor dysfunction in amyotrophic lateral sclerosis: a comprehensive review. Arch Neurol 2011; 68:857–861
269.
Lewis AJ, Gawel MJ: Diffuse Lewy body disease with dementia and oculomotor dysfunction. Mov Disord 1990; 5:143–147
270.
Bunney WE, Bunney BG: Evidence for a compromised dorsolateral prefrontal cortical parallel circuit in schizophrenia. Brain Res Brain Res Rev 2000; 31:138–146
271.
Callicott JH, Bertolino A, Mattay VS, et al: Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb Cortex 2000; 10:1078–1092
272.
Zald DH, McHugo M, Ray KL, et al: Meta-analytic connectivity modeling reveals differential functional connectivity of the medial and lateral orbitofrontal cortex. Cereb Cortex 2014; 24:232–248
273.
Burguière E, Monteiro P, Mallet L, et al: Striatal circuits, habits, and implications for obsessive-compulsive disorder. Curr Opin Neurobiol 2015; 30:59–65
274.
Volkow ND, Fowler JS: Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex. Cereb Cortex 2000; 10:318–325
275.
Bush G, Luu P, Posner MI: Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci 2000; 4:215–222
276.
Brennan BP, Tkachenko O, Schwab ZJ, et al: An examination of rostral anterior cingulate cortex function and neurochemistry in obsessive-compulsive disorder. Neuropsychopharmacology 2015; 40:1866-1876
277.
Umemoto A, Lukie CN, Kerns KA, et al: Impaired reward processing by anterior cingulate cortex in children with attention deficit hyperactivity disorder. Cogn Affect Behav Neurosci 2014; 14:698–714
278.
Zgaljardic DJ, Borod JC, Foldi NS, et al: An examination of executive dysfunction associated with frontostriatal circuitry in Parkinson’s disease. J Clin Exp Neuropsychol 2006; 28:1127–1144
279.
Nambu A: Functional circuitry of the basal ganglia, in Deep Brain Stimulation for Neurological Disorders Edited by Itakura T. New York, Springer, 2015, pp 1–11
280.
Frank MJ: Computational models of motivated action selection in corticostriatal circuits. Curr Opin Neurobiol 2011; 21:381–386
281.
Richman JS, Moorman JR: Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000; 278:H2039–H2049

Information & Authors

Information

Published In

Go to The Journal of Neuropsychiatry and Clinical Neurosciences
Go to The Journal of Neuropsychiatry and Clinical Neurosciences
The Journal of Neuropsychiatry and Clinical Neurosciences
Pages: 101 - 114
PubMed: 29183233

History

Received: 10 February 2017
Revision received: 22 May 2017
Revision received: 28 July 2017
Accepted: 4 August 2017
Published online: 29 November 2017
Published in print: Spring 2018

Keywords

  1. Basal Ganglia Disorders
  2. basal ganglia
  3. Parkinson-s Disease
  4. entropy
  5. movement disorders

Authors

Details

Daniela S. Andres, M.D., Ph.D. [email protected]
From the Science and Technology School, National University of San Martin, Buenos Aires, Argentina (DSA); the Department of Neurology, University of South Alabama, Mobile, Ala. (OD); and the Division of System Neurophysiology, National Institute for Physiological Sciences, Okazaki, Japan (OD).
Olivier Darbin, Ph.D. [email protected]
From the Science and Technology School, National University of San Martin, Buenos Aires, Argentina (DSA); the Department of Neurology, University of South Alabama, Mobile, Ala. (OD); and the Division of System Neurophysiology, National Institute for Physiological Sciences, Okazaki, Japan (OD).

Notes

Send correspondence to Dr. Andres; Email: [email protected] or Dr. Darbin; Email: [email protected]

Author Contributions

Drs. Andres and Darbin contributed equally to this study.

Funding Information

Ministry of Science, Technology and Innovation, Argentina:
National Institute for Physiological Sciences10.13039/501100006323:
College of Medicine, University of South Alabama:
Dr. Andres is supported by the Argentina Ministry of Science, Technology and Innovation, and by the Argentina National University of San Martin. Dr. Darbin is supported by the College of Medicine, University of South Alabama, Mobile, Ala., and the National Institute for Physiological Sciences, Okazaki, Japan.

Metrics & Citations

Metrics

Citations

Export Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Format
Citation style
Style
Copy to clipboard

View Options

View options

PDF/EPUB

View PDF/EPUB

Login options

Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.

Personal login Institutional Login Open Athens login
Purchase Options

Purchase this article to access the full text.

PPV Articles - Journal of Neuropsychiatry and Clinical Neurosciences

PPV Articles - Journal of Neuropsychiatry and Clinical Neurosciences

Not a subscriber?

Subscribe Now / Learn More

PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.

Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).

Media

Figures

Other

Tables

Share

Share

Share article link

Share