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Abstract

Serious mental illness (SMI) is disabling, and current interventions are ineffective for many. This exploratory study sought to demonstrate the feasibility of applying topological data analysis (TDA) to resting-state functional connectivity data obtained from a heterogeneous sample of 235 adult inpatients to identify a biomarker of treatment response. TDA identified two groups based on connectivity between the prefrontal cortex and striatal regions: patients admitted with greater functional connectivity between these regions evidenced less improvement from admission to discharge than patients with lesser connectivity between them. TDA identified a potential biomarker of an attenuated treatment response among inpatients with SMI. Insofar as the observed pattern of resting-state functional connectivity collected early during treatment is replicable, this potential biomarker may indicate the need to modify standard of care for a small, albeit meaningful, percentage of patients.
Serious mental illness (SMI) is a significant cause of disability worldwide.1 Depression, bipolar disorder, anxiety, substance use disorders, and psychosis are among the most common disorders in this class,2 each of which is not fully understood and has much higher prevalence, cost, and burden than previously estimated.3 Unfortunately, interventions for many of these disorders are ineffective for a sizable proportion of individuals. Trials of front-line medications for patients with major depressive disorders, for example, indicate remission rates below 50%,4 and only a minority of these patients fully recover with medications alone.5 Furthermore, untreated symptoms are associated with worsening disability, suffering, and cost.6 Targeted prevention and intervention efforts are needed to reduce disability, financial burden, and mortality that these disorders confer.2
Contemporary medicine conceptualizes SMI as a brain disorder.7 Resting-state functional connectivity (RSFC) derived via functional MRI (fMRI) provides critical insight into the neural bases of these disorders.8 Across existing RSFC studies of internalizing and externalizing disorders, aberrant connectivity of the prefrontal cortex (PFC) to other brain regions is consistently associated with psychopathology. In general, internalizing disorders (e.g., depression and anxiety spectrum disorders) are related to dysfunctional connectivity between the PFC and emotion-related brain areas (e.g., amygdala9). Externalizing disorders (e.g., antisocial behavior and substance abuse), on the other hand, are characterized by dysfunctional connectivity between the PFC and brain regions associated with reward/punishment (e.g., striatum10,11). The generalizability of these findings is limited, however, by small, clinically homogenous samples that are uncharacteristic of the typically highly comorbid SMI populations seen in everyday clinical practice.
Conventional approaches used to analyze neuroimaging data frequently rely on correlational approaches with conclusions based on the degree to which parameters share patterns of variability. For example, our group found significant correlations between interhemispheric inferior frontal gyri RSFC as well as interhemispheric insula RSFC and a global measure of self-reported substance use in a large, heterogeneous sample of inpatients with SMI.12 This approach and related regression analyses have a long and established history but are limited to an a priori selection of a restricted number of parameters to avoid the known problems associated with multiple comparisons. While conservative in their conclusions, these approaches likely contribute to the relatively slow pace of advances in the understanding and treatment of SMI.
More recently, machine learning approaches have been used to create models based on neuroimaging to predict treatment response among a variety of conditions, such as depression.13 While these approaches are more complex than conventional bivariate analyses, the field tends to rely on supervised learning approaches. Small sample sizes, again, are evident across supervised as well as unsupervised machine learning approaches.13 The current study, on the other hand, demonstrated the feasibility of applying an agnostic analytic approach, topological data analysis (TDA), to RSFC data from a large (N=235), heterogeneous SMI sample collected early during the course of hospitalization to a specialty treatment facility. Patterns of connectivity between PFC and select brain regions associated with SMI were analyzed. This hypothesis-generating approach allowed for comparison of groups of patients (based on extensive patterns of connectivity) across diagnostic categories and treatment response domains.

Methods

Participants

Participants were 279 adult inpatients, voluntarily admitted to a specialty hospital (November 2012–September 2014) who consented to and completed the neuroimaging portion of the study. Of the 279 inpatients, 44 (15.3%) were excluded from the final analysis due to incomplete imaging data or incomplete clinical data. Final analyses are based on the remaining 235 inpatients with complete data.

Setting

All patients received treatment at a hospital specializing in SMI. Most had limited benefit from prior trials of psychotherapy, psychopharmacology, and/or psychiatric hospitalizations. Typical lengths of stay are 6–8 weeks, allowing for intensive psychotherapeutic and psychopharmacologic interventions in the context of a therapeutic milieu. Hospital-based interventions include: maximizing medication management, individual psychotherapy, group psychotherapy (process, psychoeducational), couples and family therapy, as well as structured leisure time activities. Chemical dependency and eating disorders evaluation and treatment supplement standard of care as indicated.

Procedures

Clinical data were collected as part of standard of care in the context of the hospital’s ongoing efforts to measure the effectiveness of treatment.14 Assessments were done throughout the course of the hospitalization; however, only data collected at admission and discharge were used in the present study.

Neuroimaging: Acquisition, Preprocessing, and Resting-State Analysis

In the neuroimaging phase of the study, participants underwent an fMRI protocol early in the course of their hospitalization; details are discussed below in the Results section. Using a Siemens 3T Trio scanner, participants were scanned in a series of MRI sequences including 1) a T1-weighted structural scan (4.5-minute structural MPRAGE sequence, TE=2.66 ms, TR=1200 ms, flip angle=12°, 256×256 matrix, 160 1-mm axial slices at 1×1×1 mm voxels) to obtain detailed anatomy and 2) resting-state fMRI for 5 minutes while participants viewed a crosshair (TE=40 ms, TR=2 seconds, flip angle=90°, 3.4×3.4×4 mm voxels). Participants were instructed to “let their mind wander.” RSFC data were preprocessed using SPM8 (The Wellcome Trust, London), including realignment to the first time series image, coregistration to the mean image, normalization to the Montreal Neurological Institute (MNI) echo planar imaging template, and smoothing with a 6-mm full width at high maximum Gaussian smoothing kernel. Individual time points with excessive movement were removed using the software ART (ARtifact detection Tools, Susan Whitfield-Gabrieli, MIT [http://web.mit.edu/swg/art/art.pdf]) utilizing the default parameters. Thus, data from all patients were included in the final analysis.
Regions of interest (ROI) for inferior, medial, and superior prefrontal cortex (frontal gyri); nucleus accumbens; putamen; amygdala; insula; anterior cingulate cortex; supplemental motor area; striatum; caudate; globus pallidus; habenula; dorsal, medial raphe; Broadmann area 25; medial vestibular cortex; septum verum; locus coeruleus; lingual gyrus; paracentral lobule; precuneus; and cuneus were created in AFNI15 using the MNI atlas. These ROIs were selected due to prior literature associating them with serious mental illness and/or because of their biological connection to areas implicated in prior research (e.g., locus coeruleus downstream connections from the habenula).16
The MatLab CONN toolbox17 was used to analyze RSFC data. Gray matter, white matter, and cerebrospinal fluid were segmented. Movement identified during preprocessing was included as six regressors of no interest; data with excessive movement were removed. Cerebrospinal fluid and white matter were also included as regressors. After processing, Fisher’s z-transformed correlation coefficients between the different seeds for each subject were identified and analyzed. All pairs of regions were analyzed, and RSFC recorded for each subject. For further details regarding the imaging protocol, see Viswanath and colleagues.12

Measures

Demographic variables and treatment histories were collected using a standardized survey.14 Structured Clinical Interview for DSM-IV Axis I [SCID-I18] and Axis II [SCID-II19] interviews were conducted by masters-level research staff.
The primary outcome measure in this study was the 12-item World Health Organization Disability Assessment Schedule 2.0 (WHO-DAS 2.020). This self-report measure of functional disability associated with illness was selected over other disease- and symptom-specific measures given that 1) significant disability is associated with SMI1; 2) The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) Disability Study Group endorsed the use of the WHODAS 2.0 to replace the Global Assessment of Functioning with the revised diagnostic classification system, emphasizing that it is the best current measure of disability for routine clinical use21; and 3) there is known diagnostic heterogeneity and significant comorbidity among the patient population treated at the study institution.22 The WHO-DAS 2.0 quantifies disability associated with illness regardless of underlying etiology or number of medical/psychiatric comorbidities,23 resulting in an ideal cross-cutting measure of treatment outcome in a multimodal treatment setting with myriad diagnostic presentations. The measure assesses six domains of functioning corresponding to International Classification of Functioning, Disability and Health codes: cognition, mobility, self-care, getting along, life activities and participation. The measure has sound psychometric properties and is widely used.20,23 Raw scores were used for all analyses.
The Patient Health Questionnaire-15 (PHQ-1524) is a broadband, self-report measure of somatic complaints with sound psychometric properties that was included in subsequent analyses as a covariate (discussed below). Physical/medical comorbidities are common among individuals with SMI,25 including among patients treated at the study hospital,22 and have the potential to significantly contribute to disability.

Data Analyses

TDA was done using the Ayasdi 3.0 platform (Ayasdi Inc., Menlo Park, Calif.), allowing for an exploration of the shape of the RSFC data. Briefly, TDA is a process of capturing geometric information about data in the form of topological summaries, represented as networks on the Ayasdi 3.0 platform.26 Unlike traditional networks where nodes represent individuals and edges between nodes encode a measure of similarity between patients, each node in a topological network contains a number of patients who are similar over select characteristics—in this analysis, RSFC parameters. Nodes in the network are connected to one another via edges; nodes contain participants who are similar. The topological analyses and subsequent creation of the network are contingent on two parameters: metric and lens. Metrics are a measurement of similarity and measure the distance between two points, typically between rows in a data set. Lenses are filters that convert the data set into a vector, such that each row of data in the data set provides a real number in the vector. Essentially, lenses convert every row in a data set into a single number. Lenses can come from geometry, statistics or any other branch of mathematics. Lenses are used to create overlapping bins in the data set and allow the data to be clustered. Metrics and lenses are used together to complete the topological analysis. Details about the mathematical underpinnings of the construction of the topological network can be found elsewhere.26,27
Topological networks were generated based on 204 possible connections between the right and left PFC from a total of 648 pairing of ROIs that have been associated with SMI as detailed above. They were created using six default combinations of metric and lens parameters on the Ayasdi 3.0 platform, including 1) correlation and multidimensional scaling (coordinates 1 and 2); 2) correlation and L-infinity centrality; 3) correlation and neighborhood lens 1 and 2; 4) variance normalized Euclidean and principal component analysis (coordinates 1 and 2); 5) variance normalized Euclidean and L-infinity centrality; and 6) variance normalized Euclidean and neighborhood lens 1 and 2. Details about the six combinations of metric and lens are published elsewhere.26,27 Strength of association among RSFC brain regions was based on Kolmogorov-Smirnov (K-S) test statistic with associated p value. K-S statistics range in value from 0 to 1 with higher values being rarer than lower values and indicative of larger difference between groups.28,29 Given the high number of possible connections among brain regions and the increased likelihood of spurious findings, we employed a permutation-based correction to family wise error rate (family-wise error-corrected α=0.05) to determine statistically-significant RSFC parameters that contributed to the generation of the topological network.
Patients who shared the same or neighboring nodes in a TDA network were identified as being similar and were grouped together; they were then compared with the remaining sample. Comparisons between these two groups of patients were done across clinical data collected at admission using independent samples t tests and Fisher’s exact test for continuous and categorical variables, respectively. Group differences in treatment outcome (discharge scores on the WHO-DAS 2.0) were examined using analysis of covariance (ANCOVA). Given their potential to bias discharge findings, ANCOVA models employed to evaluate treatment outcomes included the following covariates: admission levels of disability based on the WHO-DAS 2.0, admission levels of somatic complaints based on the PHQ-15, and any other potential differences in sociodemongraphic and clinical characteristics observed at admission. These analyses were conducted using SAS/STAT software, Version 9.3 (Cary, N.C.).

Ethics

This study conforms to guidelines set forth in the latest version of the Declaration of Helsinki. The Baylor College of Medicine’s Institutional Review Board approved the study design. Participants provided informed consent to participate in the study after receiving full explanation of all procedures.

Results

Of the six TDA analytic strategies evaluated, only variance normalized Euclidean and L-infinity centrality provided separation of patients based on connectivity between PFC and select brain regions. Two groups were represented: group 1 (N=13; 5.5%) and group 2 (N=222; 94.5%). There was no difference between group 1 (20.2±16.0 days) and group 2 (24.3±22.8 days) in duration of time that elapsed between date of admission and date of the fMRI (t=1.3, df=233, p=0.19). Diffuse, global RSFC drove the observed grouping of patients. Of note, connectivity between PFC and striatal brain regions accounted for 32.8% (41/125) of the significant differences between groups across RSFC data, including the most prominent (K-S≥0.90) of the connectivity parameters, between the right inferior PFC and right striatum (K-S=0.9009, p<0.0001) (Table 1). As an exemplar of the RSFC derived groupings, Figure 1 represents color-coded grouping of patients based on connectivity between right inferior PFC and right striatum.
TABLE 1. Resting-State Functional Connectivity Pairings Contributing to Grouping of Patients
Region of Interest 1Region of Interest 2Kolmogorov-Smirnov Test StatisticFamily-Wise Error-Corrected p ValueConnectivity From Prefrontal Cortex [PFC] to Striatal Regions
Right inferior PFCRight striatum0.9009<0.0001Yes
Right medial PFCRight striatum0.8375<0.0001Yes
Left inferior PFCRight striatum0.83330.0033Yes
Left inferior PFCLeft striatum0.81530.003Yes
Left ACCRight putamen0.81500.0032 
Right inferior PFCLeft superior PFC0.80630.0035 
Right inferior PFCLeft striatum0.7973<0.0001Yes
Right superior PFCLeft striatum0.79660.0019Yes
Left superior PFCLeft precentral gyrus0.79280.0002 
Left superior PFCRight precentral gyrus0.79280.0027 
Right medial PFCLeft striatum0.78790.0009Yes
Right ACCRight putamen0.78790.0032 
Right superior PFCRight putamen0.77890.0032Yes
Right inferior PFCRight putamen0.7741<0.0001Yes
Left ACCRight striatum0.7696<0.0001 
Right superior PFCRight precentral gyrus0.75640.0045 
Left inferior PFCLeft nAcc0.75230.0029 
Right medial PFCRight putamen0.75190.0009Yes
Left medial PFCRight caudate0.75190.0032Yes
Right medial PFCLeft caudate0.74770.0038Yes
Left medial PFCLeft striatum0.74740.001Yes
Right superior PFCLeft SMA0.74740.0042 
Left medial PFCRight putamen0.74710.0034Yes
Right medial PFCLeft GP0.73870.0144Yes
Right inferior PFCLeft ACC0.73800.0009 
Left ACCLeft striatum0.7294<0.0001 
Right superior PFCLeft precentral gyrus0.72940.0034 
Right inferior PFCRight ACC0.72040.0032 
Right superior PFCLeft putamen0.72000.0033Yes
Left inferior PFCRight nAcc0.71590.0039 
Right ACCRight striatum0.7152<0.0001 
Right precentral gyrusRight striatum0.71170.0038 
Right superior PFCRight striatum0.71140.0009Yes
Right medial PFCRight nAcc0.71140.0034 
Left medial PFCRight superior PFC0.70720.006 
Left ACCRight caudate0.70620.0039 
Left inferior PFCRight putamen0.70240.0037Yes
Left superior PFCRight putamen0.69720.0037Yes
Left inferior PFCLeft putamen0.69330.0039Yes
Right medial PFCRight precentral gyrus0.69330.0045 
Left precentral gyrusRight striatum0.68880.0036 
Left medial PFCRight nAcc0.68810.0037 
Right ACCLeft striatum0.68470.002 
Left medial PFCRight precentral gyrus0.68470.0036 
Right medial PFCRight caudate0.68400.0034Yes
Right superior PFCRight nAcc0.67980.0039 
Left inferior PFCLeft ACC0.67500.0034 
Left ACCLeft precentral gyrus0.67500.0034 
Left inferior PFCRight ACC0.67500.0036 
Left ACCRight precentral gyrus0.67050.0034 
Right inferior PFCLeft caudate0.66630.0093Yes
Right ACCLeft precentral gyrus0.66150.0033 
Right inferior PFCLeft GP0.66150.0101Yes
Right ACCLeft precentral gyrus0.65700.0035 
Left medial PFCRight striatum0.65630.0002Yes
Left medial PFCLeft caudate0.65210.0037Yes
Left ACCLeft caudate0.65180.0038 
Right inferior PFCRight precentral gyrus0.64860.0043 
Left inferior PFCRight superior PFC0.64830.0159 
Right inferior PFCLeft medial PFC0.64410.0079 
Right amygdalaRight inferior PFC0.64410.0416 
Right medial PFCLeft superior PFC0.63480.0038 
Right amygdalaLeft inferior PFC0.63060.0345 
Right medial PFCLeft precentral gyrus0.62580.006 
Right inferior PFCLeft putamen0.62540.0028Yes
Right nAccRight precentral gyrus0.62510.0381 
Left superior PFCLeft putamen0.62470.0042Yes
Left superior PFCRight striatum0.62020.0034Yes
Left precentral gyrusRight caudate0.61680.0239 
Right amygdalaRight medial PFC0.60810.0163 
Right amygdalaLeft medial PFC0.60780.0097 
Left medial PFCLeft superior PFC0.60740.0112 
Left medial PFCLeft precentral gyrus0.60330.0033 
Left inferior PFCRight caudate0.59880.0371Yes
Right superior PFCRight caudate0.59840.0041Yes
Left medial PFCLeft putamen0.59840.0052Yes
Left amygdalaLeft inferior PFC0.59810.025 
Left medial PFCLeft nAcc0.59770.0117 
Right inferior PFCRight caudate0.59420.0077Yes
Left superior PFCRight nAcc0.59390.0292 
Left ACCLeft putamen0.59320.0034 
Left superior PFCRight superior PFC0.58940.0065 
Right superior PFCLeft superior PFC0.58940.0065 
Right inferior PFCRight nAcc0.58040.0045 
Left nAccRight putamen0.57970.0037 
Right insulaRight striatum0.57970.0142 
Right medial PFCLeft putamen0.57620.0039Yes
Right inferior PFCLeft nAcc0.57520.0034 
Right superior PFCLeft GP0.57100.0073Yes
Right inferior PFCRight superior PFC0.56310.0144 
Right ACCLeft caudate0.56200.0147 
Right superior PFCLeft caudate0.56130.0054Yes
Right medial PFCLeft ACC0.55790.007 
Right inferior PFCLeft accumbens0.55720.006 
Right medial PFCLeft nAcc0.55370.032 
Right ACCLeft putamen0.55340.0045 
Left medial PFCRight ACC0.55300.0454 
Right precentral gyrusRight putamen0.54950.0419 
Right inferior PFCRight GP0.54920.0079Yes
Left superior PFCLeft SMA0.54400.0065 
Left accumbensRight putamen0.53980.023 
Right ACCRight caudate0.53920.0086 
Left nAccLeft putamen0.53880.0082 
Right amygdalaLeft nAcc0.53530.0097 
Right accumbensRight precentral gyrus0.53530.0139 
Left medial PFCLeft ACC0.53530.0277 
Left superior PFCLeft striatum0.53470.0034Yes
Right precentral gyrusRight caudate0.53150.0397 
Right inferior PFCRight insula0.53050.0383 
Right inferior PFCLeft insula0.52980.0167 
Left inferior PFCLeft accumbens0.52600.0051 
Right precentral gyrusLeft striatum0.52180.0066 
Right precentral gyrusLeft putamen0.51770.0156 
Left precentral gyrusLeft striatum0.51700.0081 
Left amygdalaLeft medial PFC0.51630.0222 
Left inferior PFCRight inferior PFC0.51630.0468 
Right inferior PFCLeft inferior PFC0.51630.0468 
Right accumbensLeft precentral gyrus0.51250.0312 
Left superior PFCRight caudate0.51210.0354Yes
Left superior PFCLeft GP0.51180.0367Yes
Right inferior PFCRight accumbens0.50000.0067 
Left accumbensLeft putamen0.50000.043 
Left inferior PFCLeft caudate0.46360.0154Yes
Left superior PFCLeft caudate0.45050.0403Yes
Left precentral gyrusLeft putamen0.45010.0274 
a
ACC=anterior cingulate cortex; GP=globus pallidus; nAcc=nucleus accumbens; SMA=supplemental motor area.
FIGURE 1. Topological Data Analysis (TDA) Results Between Group 1 and Group 2a
a Group 1 (N=13) in red/orange versus group 2 (N=222) in blue/green; Kolmogorov-Smirnov test=0.9009, p<4.25×10−8. Warmer colors (reds, oranges) represent greater connectivity between the two regions; whereas, cooler colors (blues, greens) represent lesser connectivity between the two brain regions.
The study sample completed all self-report assessments within days of their admission; group 1 (4.0±5.6 days) did not significantly differ from group 2 (4.4±6.9 days) in duration of time that elapsed between date of admission and date of assessment (t=0.22, df=233, p=0.83). On average patients met criteria for more than 3 axis I disorders at admission. Depressive, anxiety and substance use disorders were most common. Bipolar spectrum disorders were evident in almost 20% of the sample. Psychotic spectrum disorders were relatively less common but represented. Almost 40% of the sample met criteria for at least one personality disorder. Patients had required considerable previous treatment: multiple previous therapists, psychiatrists/prescription providers, acute psychiatric hospitalizations, and extended (>5 days) psychiatric hospitalizations. Between group (group 1 versus group 2) comparisons indicated no significant differences across sociodemographic and prior service utilization characteristics (Table 2). Although borderline personality disorder was most common, group 1 was more likely to receive a diagnosis of antisocial personality disorder (15.38%) than group 2 (1.80%), p=0.038.
TABLE 2. Comparison of Sociodemographic, Psychiatric, and Service Utilization Characteristics Between Prefrontal Cortex Resting-State Functional Connectivity-Identified Groups
CharacteristicGroup 1 (N=13)Group 2 (N=222)p
Sociodemographic   
Age (mean±standard deviation [years])30.92±11.530.76±11.640.961
Sex, % (N) male69.23 (9)57.21 (127)0.165
Ethnicity, % (N) White84.62 (11)89.59 (198)0.546
Marital status, % (N) single/never married69.23 (9)68.35 (149)0.532
Education, % (N) some college or greater100.0 (13)90.5 (201)0.656
Vocational status, % (N) unemployed (30 days)69.23 (9)57.47 (128)0.300
Psychiatric diagnostic   
Axis I disorders (mean±standard deviation)2.62±2.023.06±1.650.348
Axis II disorders (mean±standard deviation)0.62±0.960.63±0.860.958
Substance use disorders, % (N)38.46 (5)64.68 (141)0.076
Major depressive disorders, % (N)53.85 (7)66.06 (144)0.381
Bipolar spectrum, % (N)15.38 (2)20.18 (44)>0.999
Anxiety spectrum, % (N)69.23 (9)60.09 (131)0.574
Psychotic spectrum, % (N)15.38 (2)15.38 (12)0.181
Antisocial personality disorder, % (N)15.38 (2)1.8 (4)0.038
Avoidant personality disorder, % (N)0 (0)19.37 (43)0.133
Borderline personality disorder, % (N)30.77 (4)20.72 (46)0.483
Any personality disorder, % (N)38.5 (5)43.6 (99)0.781
Mental health service utilization   
Outpatient therapists (lifetime)2.77±2.623.89±2.880.176
Psychopharmacologists (lifetime)2.46±1.612.9±2.260.496
Hospitalizations for acute psychiatric care (lifetime)1.54±1.901.26±4.450.827
Hospitalizations for extended psychiatric care (lifetime)0.77±1.090.94±2.160.776
Length of stay (mean±standard deviation)45.69±17.8250.98±16.480.264
After controlling for self-reported disability at admission (p<0.0001), diagnosis of antisocial personality disorder (p=0.046), and somatic complaints (p=0.582), results indicate that group 1 (N=13) evidenced less improvement than group 2 (N=222) from admission to discharge on the WHO-DAS 2.0 total score (F=4.35, df=1, 230, p=0.0381). Relative to their age-matched, normative peers,30 group 1 moved from the severe range of disability (90–95th percentile; 9.92±7.81) to the moderate range of disability (85–89th percentile; 6.62±7.12), while group 2 moved from the extreme range of disability (95–99th percentile; 15.02±9.26) to the mild range of disability (75th–84th percentile; 4.82±5.37). (Figure 2). Qualitatively, 30.8% of group 1 scored in the remission category at discharge (normal range of functioning in terms of WHO-DAS 2.0), whereas 46.0% of group 2 scored in the remission category at discharge. Additional analyses indicated that group 1 evidenced less improvement at discharge across two of six domains of disability on the WHO-DAS 2.0 after controlling for the corresponding admission disability domain, diagnosis of antisocial personality disorder, and somatic complaints. See Table 3 for details. In absolute terms, group 1 (3.3±5.19) evidenced approximately one-third of the change in WHO-DAS 2.0 total score from admission to discharge compared with group 2 (10.19±7.95), t(233)=3.08, p=0.0023. Qualitatively, 30.8% of group 1 scored in the remission category at discharge (normal range of functioning in terms of WHO-DAS 2.0), whereas 46.0% of group 2 scored in the remission category at discharge.
FIGURE 2. Comparison of Response to Treatment Between Group 1 (Higher Degree of Connectivity Between Prefrontal Cortex [PFC] and Striatal Brain Regions) Compared With Group 2a
a From admission to discharge, group 1 moved from the severe range of disability (9.92±7.81) to the moderate range of disability (6.62±7.12), while group 2 moved from the extreme range of disability (15.02±9.26) to the mild range of disability (4.82±5.37). WHO-DAS 2.0=World Health Organization Disability Assessment Schedule, Version 2.0.
TABLE 3. Group 1 Versus Group 2 Comparison of Disability Domains on the World Health Organization Disability Assessment Schedule 2.0
DomainGroup 1 (N=13) (Mean±Standard Deviation)Group 2 (N=222) (Mean±Standard Deviation)p
Cognition  0.867
 Admission2.38±2.262.72±2.12 
 Discharge1.00±1.680.973±1.20 
Mobility  0.466
 Admission0.31±0.851.38±1.77 
 Discharge0.54±1.130.50±1.15 
Self-care  0.023
 Admission0.54±1.450.82±1.57 
 Discharge0.69±1.110.20±0.71 
Getting along  0.078
 Admission1.54±1.902.81±2.08 
 Discharge1.31±1.381.00±1.36 
Life activities  0.044
 Admission2.46±1.763.35±2.28 
 Discharge1.38±1.390.90±1.23 
Participation  0.067
 Admission2.69±2.063.95±2.24 
 Discharge1.69±1.751.26±1.40 

Discussion

Concerted multidisciplinary efforts have identified a number of biomarkers of SMI; yet, robust, replicable, and clinically actionable biomarkers remain elusive.7,31 Through an agnostic, exploratory analytical approach, the current study examined RSFC of the PFC in the largest, clinically heterogeneous SMI population collected to date. This novel analytic approach identified a subset of patients who evidenced diffuse, global hyperconnectivity with primarily stronger connectivity between the PFC and striatal regions. Subsequent analyses suggest that patients in the group with diffuse, global hyper-connectivity were more likely to meet formal criteria for an antisocial personality disorder diagnosis compared with patients with lesser connectivity. Additionally, hyper-connectivity was associated with an attenuated treatment response after weeks of intensive, multimodal treatment – even after controlling for baseline characteristics (e.g., baseline disability, presence of antisocial personality disorder, baseline somatic complaints). Thus, the attenuated treatment response cannot be accounted for solely by the presence of antisocial personality disorder. Despite receiving the same intensive treatment, a small group of patients experienced an attenuated treatment response and discharged in the moderate range of disability. Only 30.8% of this group would be classified as being in remission upon discharge (i.e., falling in the normal range of functioning on the WHO-DAS 2.0) compared with 46.0% patients in the other group. If these findings can be replicated, the policy implications of a biomarker of differential treatment response (15.2% fewer patients remitting) potentially could have significant repercussions for the broader SMI patient population.
Recently, dopaminergic-mediated pathways between the PFC and striatum have been implicated in neurobiological models of anhedonia in depression. Treadway and Zald32 suggested that motivational aspects of the reward circuitry could distinguish varieties of anhedonia based on deficits in pleasure and motivation. They introduced the term “decisional anhedonia” to address the influence of anhedonia on reward decision-making. Perhaps a high degree of connectivity between the PFC and striatum reflects a lack of flexibility in the face of potentially reinforcing stimuli and is reflective of a treatment-refractory neurobiology among individuals with SMI, especially among individuals meeting criteria for antisocial personality disorder. Alternatively (or perhaps additionally), this hyperconnectivity may be a marker of maladaptive, thrill-seeking personality traits, as is common among many individuals with antisocial personality disorder or substance use disorders.11 Insofar as the observed pattern of RSFC collected early during treatment is replicable in a future sample, this potential biomarker may indicate the need for modification of standard of care for patients who are likely to evidence an attenuated treatment response such as the use of pharmacogenomics testing to guide medical decision-making (e.g., evaluation of polymorphisms that may affect dopaminergic activity33;).
Findings from this study also may be useful in advancing the overarching goal of the NIMH Research Domain Criteria (RDoC) initiative to develop an empirically derived system for diagnosing and treating mental disorders based on genetic, neural and behavioral data.34 Specifically, RDoC is calling for a new approach to investigating psychopathology beyond traditional diagnostic boundaries because studies evaluating the latent structure of adult personality pathology at the symptom level have found only modest support for discrete DSM-based disorders (see35 for a review). Indeed, there has been a growing interest in considering models that evaluate general factors that account for both common variances shared across diagnoses and unique sources of variance that may represent more specific forms of psychopathology.36,37 As yet, putative meta-structures of psychopathology have yet to be validated with biological data. It is against this background that the analytic approach of the current study has potential value.
While this study has a number of strengths including a relatively large, heterogeneous sample typical of real world patients, with extensive imaging data and objective measures of clinical functioning, limitations must be acknowledged. The data analytic approach was by design exploratory and could have yielded spurious findings given the sheer number of RSFC brain regions examined. However, selecting PFC connections to select brain regions, correcting for multiple comparisons using among the most balanced correction procedures (i.e., permutation-based correction of family-wise error),38 as well as limiting interpretation of significant findings to a very high threshold for statistical significance were a priori decisions to increase the likelihood of finding a true signal. Nonetheless, replication of these findings is critical before using connectivity data to guide medical decision-making on the individual level. Data collection for a validation study is currently underway; however, a large sample will be necessary to be sufficiently powered to replicate findings given the limited numbers of patients with an attenuated treatment response (5.5%) compared with the rest of the sample. We anticipate recruiting another 300 participants for the validation study and will use findings from this study to employ more traditional analytic approaches based on a priori hypotheses. Future analytic approaches with a large enough sample may also include appropriately selected cross-validation techniques.39 Additionally, though it is possible that extreme values and violations of underlying assumptions of normally distributed data might have biased findings, this is unlikely given 1) the large sample size (N=235) and associated robustness that it provides to the influence of outliers40; 2) reliance on the nonparametric, K-S statistics to establish strength of association given that it is essentially an analysis of ranks and does not assume an underlying distribution to the data28; and 3) post hoc analyses of all of the RSFC data revealed no violations of the underlying assumptions of normality (Shapiro-Wilks) and only one significant difference (out of a possible 125 RSFC parameters) between groups’ standard deviations (F’; right inferior PFC to left superior PFC, p=0.0001). Despite limitations, the exploratory, novel data analytic approach applied to RSFC data in this study represents a paradigm shift from more traditional, hypothesis-driven approaches to explore clinical phenomenology and evaluation of interventions. Such approaches may be necessary to increase the pace of discovery to address the global burden of SMI.

Acknowledgments

The authors thank the team of Ayasdi Inc. (Gunnar Carlsson, Devi Ramanan, and Ajith Warrier) for clinical assistance provided in understanding topological data analysis, as well as application of their proprietary data analytics platform.

Footnote

The study follows the guidelines on good publication practices. The study sponsors were not involved in any aspect of the research activities and did not approve the specific protocol or the article.

References

1.
Whiteford HA, Degenhardt L, Rehm J, et al: Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 2013; 382:1575–1586
2.
Prince M, Patel V, Saxena S, et al: No health without mental health. Lancet 2007; 370:859–877
3.
Vigo D, Thornicroft G, Atun R: Estimating the true global burden of mental illness. Lancet Psychiatry 2016; 3:171–178
4.
Thase ME: Bipolar depression: issues in diagnosis and treatment. Harv Rev Psychiatry 2005; 13:257–271
5.
Rush AJ, Trivedi MH: Treating depression to remission. Psychiatr Ann 1995; 25:704–711
6.
Fowler JC, Oldham JM: Co-occurring disorders and treatment complexity. Focus 2013; 11:123–128
7.
Insel T, Collins PY, Hyman SE: Darkness invisible. Foreign Aff 2015; 94:18–19
8.
Vaidya CJ, Gordon EM: Phenotypic variability in resting-state functional connectivity: current status. Brain Connect 2013; 3:99–120
9.
Qin S, Young CB, Duan X, et al: Amygdala subregional structure and intrinsic functional connectivity predicts individual differences in anxiety during early childhood. Biol Psychiatry 2014; 75:892–900
10.
Cohn MD, Pape LE, Schmaal L, et al: Differential relations between juvenile psychopathic traits and resting state network connectivity. Hum Brain Mapp 2015; 36:2396–2405
11.
Volkow ND, Fowler JS, Wang GJ, et al: Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology 2009; 56(suppl 1):3–8
12.
Viswanath H, Velasquez KM, Savjani R, et al: Interhemispheric insular and inferior frontal connectivity are associated with substance abuse in a psychiatric population. Neuropharmacology 2015; 92:63–68
13.
Patel MJ, Khalaf A, Aizenstein HJ: Studying depression using imaging and machine learning methods. Neuroimage Clin 2015; 10:115–123
14.
Fowler JC, Allen JG, Oldham JM, et al: Exposure to interpersonal trauma, attachment insecurity, and depression severity. J Affect Disord 2013; 149:313–318
15.
Cox RW: AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996; 29:162–173
16.
Salas R, Baldwin P, de Biasi M, et al: BOLD responses to negative reward prediction errors in human habenula. Front Hum Neurosci 2010; 4:36
17.
Whitfield-Gabrieli S, Nieto-Castanon A: Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2012; 2:125–141
18.
First MB, Spitzer RL, Gibbon M, et al: Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition With Psychotic Screen (SCID-I/P W/ PSY SCREEN). New York, Biometrics Research, New York State Psychiatric Institute, 2002
19.
First MB, Gibbon M, Spitzer RL, et al: Structured Clinical Interview for DSM-IV Axis II Personality Disorders, (SCID-II). Washington, DC, American Psychiatric Publishing, 1997
20.
World Health Organization: World Health Organization Disability Assessment Schedule 2.0. http://www.who.int/classifications/icf/whodasii/en/
21.
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Washington, DC, American Psychiatric Publishing, 2013
22.
Madan A, Clapp J, Osborne P, et al: Improvements in somatic complaints among individuals with serious mental illness receiving treatment in a psychiatric hospital. Psychosom Med 2016; 78:271–280
23.
Ustun TB, Kostanjsek N, Chatterji S, et al: Measuring Health and Disability: Manual for WHO Disability Assessment Schedule (WHODAS 2.0). Geneva, World Health Organization, 2010
24.
Kroenke K, Spitzer RL, Williams JB: The PHQ-15: validity of a new measure for evaluating the severity of somatic symptoms. Psychosom Med 2002; 64:258–266
25.
DE Hert M, Correll CU, Bobes J, et al: Physical illness in patients with severe mental disorders, I: prevalence, impact of medications and disparities in health care. World Psychiatry 2011; 10:52–77
26.
Lum PY, Singh G, Lehman A, et al: Extracting insights from the shape of complex data using topology. Sci Rep 2013; 3:1236
27.
Carlsson G: Topology and data. Bull Am Math Soc 2009; 46:255–308
28.
Corder GW, Foreman DI: Nonparametric Statistics: A Step-by-Step Approach. Hoboken, NJ, John Wiley & Sons, 2014
29.
Metha S: Statistics Topics. Charleston, SC, CreateSpace Independent Publishing Platform, 2014
30.
Andrews G, Kemp A, Sunderland M, et al: Normative data for the 12 item WHO Disability Assessment Schedule 2.0. PLoS One 2009; 4:e8343
31.
Oquendo MA, McGrath P, Weissman MM: Biomarker studies and the future of personalized treatment for depression. Depress Anxiety 2014; 31:902–905
32.
Treadway MT, Zald DH: Reconsidering anhedonia in depression: lessons from translational neuroscience. Neurosci Biobehav Rev 2011; 35:537–555
33.
Madan A, Walker CR, Weinstein B, et al: Pharmacogenomics in practice: a case report of personalized inpatient psychiatric care. Pharmacogenomics 2015; 16:433–439
34.
Cuthbert BN, Insel TR: Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med 2013; 11:126
35.
Sheets ES, Craighead WE: Comparing chronic interpersonal and noninterpersonal stress domains as predictors of depression recurrence in emerging adults. Behav Res Ther 2014; 63:36–42
36.
Caspi A, Houts RM, Belsky DW, et al: The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin Psychol Sci 2014; 2:119–137
37.
Sharp C, Wright AG, Fowler JC, et al: The structure of personality pathology: Both general (‘g’) and specific (‘s’) factors? J Abnorm Psychol 2015; 124:387–398
38.
Bennett CM, Wolford GL, Miller MB: The principled control of false positives in neuroimaging. Soc Cogn Affect Neurosci 2009; 4:417–422
39.
Arlot S, Celisse A: A survey of cross-validation procedures for model selection. Stat Surv 2010; 4:40–79
40.
Tabachnick BG, Fidell LS: Using Multivariate Statistics. Needham Heights, Mass, Allyn & Bacon, 2001

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: 275 - 283
PubMed: 28238273

History

Received: 23 September 2016
Revision received: 13 October 2016
Accepted: 13 October 2016
Published online: 27 February 2017
Published in print: Summer 2017

Keywords

  1. Diagnosis and Classification in Neuropsychiatry
  2. Imaging Techniques
  3. Outcome Studies Biological/Pharmacological
  4. Research Design and Methodology

Authors

Details

Alok Madan, Ph.D., M.P.H. [email protected]
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
J. Christopher Fowler, Ph.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
Michelle A. Patriquin, Ph.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
Ramiro Salas, Ph.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
Philip R. Baldwin, Ph.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
Kenia M. Velasquez, M.S.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
Humsini Viswanath, M.P.H.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
David L. Molfese, Ph.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
Carla Sharp, Ph.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
Jon G. Allen, Ph.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
Susan Hardesty, M.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
John M. Oldham, M.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).
B. Christopher Frueh, Ph.D.
From the Menninger Clinic, Houston, Tex. (AM, CF, MAP, CS, JGA, SH, JMO, BCF); the Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tex. (AM, JCF, MAP, RS, PRB, KMV, HV, DLM, JGA, SH, JMO); the Department of Psychology, University of Houston, Houston, Tex. (CS); and the Department of Psychology, University of Hawaii, Hilo, Hawaii (BCF).

Notes

Send correspondence to Dr. Madan; e-mail: [email protected]

Competing Interests

The authors report no financial relationships with commercial interests.

Funding Information

Vivian L. Smith Foundation:
The Brown Foundation, Inc. of Houston, Texas:
Menninger Clinic Foundation:
McNair Medical Institute:
Texas Medical Center:
Supported in part by the Menninger Clinic Foundation, McNair Medical Institute, Texas Medical Center, the Vivian L. Smith Foundation, and the Brown Foundation, Inc. of Houston, Texas. Drs. Frueh and Madan are McNair Scholars.

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