Source-based morphometry of gray matter volume in men with first-episode schizophrenia

Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder

Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance personalized suicide control efforts. This study used unstructured psychiatric charts and brain magnetic resonance imaging (MRI) records from a psychiatric outpatient clinic to develop a machine learning-based suicidal thought classification model. The study included 152 patients with new depressive episodes for development and 58 patients from a geographically different hospital for validation. We developed an eXtreme Gradient Boosting (XGBoost)-based classification models according to the combined types of data: independent components-map weightings from brain T1-weighted MRI and topic probabilities from clinical notes. Specifically, we used 5 psychiatric symptom topics and 5 brain networks for models. Anxiety and somatic symptoms topics were significantly more common in the suicidal group, and there were group differences in the default mode and cortical midline networks. The clinical symptoms plus structural brain patterns model had the highest area under the receiver operating characteristic curve (0.794) versus the clinical notes only and brain MRI only models (0.748 and 0.738, respectively). The results were consistent across performance metrics and external validation. Our findings suggest that focusing on personalized neuroimaging and natural language processing variables improves evaluation of suicidal thoughts.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease

OBJECTIVE

Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI).

METHODS

We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population.

RESULTS

The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier.

INTERPRETATION

Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Investigating sexual dimorphism in human brain structure by combining multiple indexes of brain morphology and source-based morphometry

Computational morphometry of magnetic resonance images represents a powerful tool for studying macroscopic differences in human brains. In the present study (N participants = 829), we combined different techniques and measures of brain morphology to investigate one of the most compelling topics in neuroscience: sexual dimorphism in human brain structure. When accounting for overall larger male brains, results showed limited sex differences in gray matter volume (GMV) and surface area. On the other hand, we found larger differences in cortical thickness, favoring both males and females, arguably as a result of region-specific differences. We also observed higher values of fractal dimension, a measure of cortical complexity, for males versus females across the four lobes. In addition, we applied source-based morphometry, an alternative method for measuring GMV based on the independent component analysis. Analyses on independent components revealed higher GMV in fronto-parietal regions, thalamus and caudate nucleus for females, and in cerebellar- temporal cortices and putamen for males, a pattern that is largely consistent with previous findings.

Interhemispheric co-alteration of brain homotopic regions

Asymmetries in gray matter alterations raise important issues regarding the pathological co-alteration between hemispheres. Since homotopic areas are the most functionally connected sites between hemispheres and gray matter co-alterations depend on connectivity patterns, it is likely that this relationship might be mirrored in homologous interhemispheric co-altered areas. To explore this issue, we analyzed data of patients with Alzheimer’s disease, schizophrenia, bipolar disorder and depressive disorder from the BrainMap voxel-based morphometry database. We calculated a map showing the pathological homotopic anatomical co-alteration between homologous brain areas. This map was compared with the meta-analytic homotopic connectivity map obtained from the BrainMap functional database, so as to have a meta-analytic connectivity modeling map between homologous areas. We applied an empirical Bayesian technique so as to determine a directional pathological co-alteration on the basis of the possible tendencies in the conditional probability of being co-altered of homologous brain areas. Our analysis provides evidence that: the hemispheric homologous areas appear to be anatomically co-altered; this pathological co-alteration is similar to the pattern of connectivity exhibited by the couples of homologues; the probability to find alterations in the areas of the left hemisphere seems to be greater when their right homologues are also altered than vice versa, an intriguing asymmetry that deserves to be further investigated and explained.

Microstructural plasticity in nociceptive pathways after spinal cord injury

OBJECTIVE

To track the interplay between (micro-) structural changes along the trajectories of nociceptive pathways and its relation to the presence and intensity of neuropathic pain (NP) after spinal cord injury (SCI).

METHODS

A quantitative neuroimaging approach employing a multiparametric mapping protocol was used, providing indirect measures of myelination (via contrasts such as magnetisation transfer (MT) saturation, longitudinal relaxation (R1)) and iron content (via effective transverse relaxation rate (R2*)) was used to track microstructural changes within nociceptive pathways. In order to characterise concurrent changes along the entire neuroaxis, a combined brain and spinal cord template embedded in the statistical parametric mapping framework was used. Multivariate source-based morphometry was performed to identify naturally grouped patterns of structural variation between individuals with and without NP after SCI.

RESULTS

In individuals with NP, lower R1 and MT values are evident in the primary motor cortex and dorsolateral prefrontal cortex, while increases in R2* are evident in the cervical cord, periaqueductal grey (PAG), thalamus and anterior cingulate cortex when compared with pain-free individuals. Lower R1 values in the PAG and greater R2* values in the cervical cord are associated with NP intensity.

CONCLUSIONS

The degree of microstructural changes across ascending and descending nociceptive pathways is critically implicated in the maintenance of NP. Tracking maladaptive plasticity unravels the intimate relationships between neurodegenerative and compensatory processes in NP states and may facilitate patient monitoring during therapeutic trials related to pain and neuroregeneration.

Brain gray matter differences among forensic psychiatric patients with psychosis and incarcerated individuals without psychosis: A source-based morphometry study

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We employed source-based morphometry to examine grey matter differences in forensic psychiatric patients with psychosis versus incarcerated controls without psychosis.

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Compared to the psychotic group, the non-psychotic group demonstrated greater loading weights in the superior, transverse, and middle temporal gyrus and the anterior cingulate.

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Compared to the non-psychotic group, the psychotic group exhibited greater loading weights in the frontal pole, precuneus, basal ganglia, thalamus, parahippocampal gyrus, and visual cortex.

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Neuroimaging investigations of offenders with psychosis ought to control for the level of psychopathic traits present.

Background

While psychosis is a risk factor for violence, the majority of individuals who perpetrate aggression do not present psychotic symptoms. Pathological aggressive behavior is associated with brain gray matter differences, which, in turn, has shown a relationship with increased psychopathic traits. However, no study, to our knowledge, has ever investigated gray matter differences in forensic psychiatric patients with psychosis compared with incarcerated individuals without psychosis matched on levels of psychopathic traits. Here, we employed source-based morphometry (SBM) to investigate gray matter differences in these two populations.

Methods

We scanned 137 participants comprising two offender subgroups: 69, non-psychotic incarcerated offenders and 68, psychotic, forensic psychiatric patients. Groups showed no difference in age, race, ethnicity, handedness, and Hare Psychopathy Checklist-Revised scores. Source-based morphometry was utilized to identify spatially distinct sets of brain regions where gray matter volumes covaried between groups. SBM is a data-driven, multivariate technique that uses independent components analysis to categorize groups of voxels that display similar variance patterns (e.g., components) that are compared across groups.

Results

SBM identified four components that differed between groups. These findings indicated greater loading weights in the superior, transverse, and middle temporal gyrus and anterior cingulate in the non-psychotic versus psychotic group; greater loading weights in the basal ganglia in the psychotic versus non-psychotic group; greater loading weights in the frontal pole, precuneus, and visual cortex among psychotic versus non-psychotic offenders; and greater loading weights in the thalamus and parahippocampal gyrus in psychotic versus non-psychotic groups.

Conclusions

Two different offender groups that perpetrate violence and show comparable levels of psychopathic traits evidenced different gray matter volumes. We suggest that future studies of violent offenders with psychosis take psychopathic traits into account to refine neural phenotypes.

SS-Detect: Development and Validation of a New Strategy for Source-Based Morphometry in Multiscanner Studies

BACKGROUND AND PURPOSE

Source-based morphometry(SBM) has been used in multicenter studies pooling magnetic resonance imaging data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multiscanner studies, and evaluated its performance relative to a conventional strategy.

METHODS

In the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking 20 different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients' and healthy controls' GMV from two scanners.

RESULTS

The outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs, whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences.

CONCLUSIONS

SS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multiscanner study.

Source-based morphometry reveals gray matter differences related to suicidal behavior in criminal offenders

Relative to the general population, criminal offenders have a higher risk of suicide. Neurobiological deficits related to suicidal behavior have been identified in the general population, but unexamined in offenders to date. We examined the association between brain morphology and suicidal behavior in adult male criminal offenders. Brain morphology was examined using voxel-based morphometry (VBM) and source-based morphometry (SBM), a multivariate alternative to VBM which analyzes brain volume in between-subject spatially independent networks. Results showed that offenders with past suicide attempts (n = 19), relative to offenders without past suicide attempts (n = 19) and non-offenders (n = 26), had reduced gray matter in an SBM component that comprised the posterior cingulate, dorsal prefrontal cortex, and amygdala. The SBM source weights were significantly associated with suicide attempts independent of other suicide risk variables (e.g., depression). VBM results were similar to the SBM results but less robust. The results reveal a potential neurobiological marker of vulnerability to suicidal behavior among criminal offenders and illustrate the utility of multivariate methods of gray matter analyses.

Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry

Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.

APOE-ε4 Shapes the Cerebral Organization in Cognitively Intact Individuals as Reflected by Structural Gray Matter Networks

Gray matter networks (GMn) provide essential information on the intrinsic organization of the brain and appear to be disrupted in Alzheimer’s disease (AD). Apolipoprotein E (APOE)-ε4 represents the major genetic risk factor for AD, yet the association between APOE-ε4 and GMn has remained unexplored. Here, we determine the impact of APOE-ε4 on GMn in a large sample of cognitively unimpaired individuals, which was enriched for the genetic risk of AD. We used independent component analysis to retrieve sources of structural covariance and analyzed APOE group differences within and between networks. Analyses were repeated in a subsample of amyloid-negative subjects. Compared with noncarriers and heterozygotes, APOE-ε4 homozygotes showed increased covariance in one network including primarily right-lateralized, parietal, inferior frontal, as well as inferior and middle temporal regions, which mirrored the formerly described AD-signature. This result was confirmed in a subsample of amyloid-negative individuals. APOE-ε4 carriers showed reduced covariance between two networks encompassing frontal and temporal regions, which constitute preferential target of amyloid deposition. Our data indicate that, in asymptomatic individuals, APOE-ε4 shapes the cerebral organization in a way that recapitulates focal morphometric alterations observed in AD patients, even in absence of amyloid pathology. This suggests that structural vulnerability in neuronal networks associated with APOE-ε4 may be an early event in AD pathogenesis, possibly upstream of amyloid deposition.

Heritability of Gray Matter Structural Covariation and Tool Use Skills in Chimpanzees (Pan troglodytes): A Source-Based Morphometry and Quantitative Genetic Analysis

Nonhuman primates, and great apes in particular, possess a variety of cognitive abilities thought to underlie human brain and cognitive evolution, most notably, the manufacture and use of tools. In a relatively large sample (N = 226) of captive chimpanzees (Pan troglodytes) for whom pedigrees are well known, the overarching aim of the current study was to investigate the source of heritable variation in brain structure underlying tool use skills. Specifically, using source-based morphometry (SBM), a multivariate analysis of naturally occurring patterns of covariation in gray matter across the brain, we investigated (1) the genetic contributions to variation in SBM components, (2) sex and age effects for each component, and (3) phenotypic and genetic associations between SBM components and tool use skill. Results revealed important sex- and age-related differences across largely heritable SBM components and associations between structural covariation and tool use skill. Further, shared genetic mechanisms appear to account for a heritable link between variation in both the capacity to use tools and variation in morphology of the superior limb of the superior temporal sulcus and adjacent parietal cortex. Findings represent the first evidence of heritability of structural covariation in gray matter among nonhuman primates.

Brain structural alterations are distributed following functional, anatomic and genetic connectivity

The pathological brain is characterized by distributed morphological or structural alterations in the grey matter, which tend to follow identifiable network-like patterns. We analysed the patterns formed by these alterations (increased and decreased grey matter values detected with the voxel-based morphometry technique) conducting an extensive transdiagnostic search of voxel-based morphometry studies in a large variety of brain disorders. We devised an innovative method to construct the networks formed by the structurally co-altered brain areas, which can be considered as pathological structural co-alteration patterns, and to compare these patterns with three associated types of connectivity profiles (functional, anatomical, and genetic). Our study provides transdiagnostical evidence that structural co-alterations are influenced by connectivity constraints rather than being randomly distributed. Analyses show that although all the three types of connectivity taken together can account for and predict with good statistical accuracy, the shape and temporal development of the co-alteration patterns, functional connectivity offers the better account of the structural co-alteration, followed by anatomic and genetic connectivity. These results shed new light on the possible mechanisms at the root of neuropathological processes and open exciting prospects in the quest for a better understanding of brain disorders.

Testing the expanded continuum hypothesis of schizophrenia and bipolar disorder. Neural and psychological evidence for shared and distinct mechanisms

Despite the traditional view of Schizophrenia (SZ) and Bipolar disorder (BD) as separate diagnostic categories, the validity of such a categorical approach is challenging. In recent years, the hypothesis of a continuum between Schizophrenia (SZ) and Bipolar disorder (BD), postulating a common pathophysiologic mechanism, has been proposed. Although appealing, this unifying hypothesis may be too simplistic when looking at cognitive and affective differences these patients display. In this paper, we aim to test an expanded version of the continuum hypothesis according to which the continuum extends over three clusters: the psychotic, the cognitive, and the affective. We applied an innovative approach known as Source-based Morphometry (SBM) to the structural images of 46 individuals diagnosed with SZ, 46 with BD and 66 healthy controls (HC). We also analyzed the psychological profiles of the three groups using cognitive, affective, and clinical tests. At a neural level, we found evidence for a shared psychotic core in a distributed network involving portions of the medial parietal and temporo-occipital areas, as well as parts of the cerebellum and the middle frontal gyrus. We also found evidence of a cognitive core more compromised in SZ, including alterations in a fronto-parietal circuit, and mild evidence of an affective core more compromised in BD, including portions of the temporal and occipital lobes, cerebellum, and frontal gyrus. Such differences were confirmed by the psychological profiles, with SZ patients more impaired in cognitive tests, while BD in affective ones. On the bases of these results we put forward an expanded view of the continuum hypothesis, according to which a common psychotic core exists between SZ and BD patients complemented by two separate cognitive and affective cores that are both impaired in the two patients' groups, although to different degrees.

Prognostic Utility of Multivariate Morphometry in Schizophrenia

Background: Voxel-based morphometry studies have repeatedly highlighted the presence of distributed gray matter changes in schizophrenia, but to date, it is not clear if clinically useful prognostic information can be gleaned from structural imaging. The suspected association between gray matter volume (GMV) and duration of psychotic illness, antipsychotic exposure, and symptom severity also limits the prognostic utility of morphometry. We address the question of whether morphometric information from patients with drug-naive first-episode psychosis can predict the linear trajectory of symptoms following early antipsychotic intervention using a longitudinal design. Method: Sixty-two first-episode, drug-naive patients with schizophrenia underwent brain magnetic resonance imaging scans at baseline, treated with antipsychotics, and rescanned after 1-year follow-up. Positive and Negative Syndrome Scale (PANSS) was used to assess their clinical manifestations. A multivariate approach to detect covariance-based network-like spatial components [Source Based Morphometry (SBM)] was performed to analyze the GMV. Paired t tests were used to study changes in the loading coefficients of GMV in the spatial components between two time points. The reduction in PANSS scores between the baseline (T0) and 1-year follow-up (T1) expressed as a ratio of the baseline scores (reduction ratio) was computed for positive, negative, and disorganization symptoms. Separate multiple regression analyses were conducted to predict the longitudinal change in symptoms (treatment response) using the loading coefficients of spatial components that differed between T0 and T1 with age, gender, duration of illness, and antipsychotic dose as covariates. We also tested the putative "toxicity" effects of baseline symptom severity on the GMV at 1 year using multiple regression analysis. Results: Of the 30 spatial components of gray matter extracted using SBM, loading coefficients of anterior cingulate cortex (ACC), insula and inferior frontal gyrus (IFG), superior temporal gyrus (STG), middle temporal gyrus (MTG), precuenus, and dorsolateral prefrontal cortex (DLPFC) reduced with time in patients. Specifically, the lower volume of insula and IFG at baseline predicted a lack of improvement in positive and disorganization symptoms. None of the symptom severity scores (positive, negative, or disorganization) at baseline independently predicted the reduced GMV at 1 year. Conclusions: The baseline deficit in a covariance-based network-like spatial component comprising of insula and IFG is predictive of the clinical course of schizophrenia. We do not find any evidence to support the notion of symptoms per se being neurotoxic to gray matter tissue. If judiciously combined with other available predictors of clinical outcome, multivariate morphometric information can improve our ability to predict prognosis in schizophrenia.

Gray matter atrophy patterns in multiple sclerosis: A 10-year source-based morphometry study

Objectives

To investigate spatial patterns of gray matter (GM) atrophy and their association with disability progression in patients with early relapsing-remitting multiple sclerosis (MS) in a longitudinal setting.

Methods

Brain MRI and clinical neurological assessments were obtained in 152 MS patients at baseline and after 10 years of follow-up. Patients were classified into those with confirmed disability progression (CDP) (n = 85) and those without CDP (n = 67) at the end of the study. An optimized, longitudinal source-based morphometry (SBM) pipeline, which utilizes independent component analysis, was used to identify eight spatial patterns of common GM volume co-variation in a data-driven manner. GM volume at baseline and rates of change were compared between patients with CDP and those without CDP.

Results

The identified patterns generally included structurally or functionally related GM regions. No significant differences were detected at baseline GM volume between the sub-groups. Over the follow-up, patients with CDP experienced a significantly greater rate of GM atrophy within two of the eight patterns, after correction for multiple comparisons (corrected p-values of 0.001 and 0.007). The patterns of GM atrophy associated with the development of CDP included areas involved in motor functioning and cognitive domains such as learning and memory.

Conclusion

SBM analysis offers a novel way to study the temporal evolution of regional GM atrophy. Over 10 years of follow-up, disability progression in MS is related to GM atrophy in areas associated with motor and cognitive functioning.

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We present a longitudinal source-based morphometry (SBM) pipeline for detecting gray matter atrophy in multiple sclerosis.

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We report a significantly greater rate of atrophy in patients developing disability progression over 10 years of follow-up.

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We show that longitudinal SBM is potentially more sensitive than voxel-based morphometry in detecting gray matter atrophy.

Brain Structural Networks Associated with Intelligence and Visuomotor Ability

Increasing evidence indicates that multiple structures in the brain are associated with intelligence and cognitive function at the network level. The association between the grey matter (GM) structural network and intelligence and cognition is not well understood. We applied a multivariate approach to identify the pattern of GM and link the structural network to intelligence and cognitive functions. Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based morphometry analysis was applied to the imaging data to extract GM structural covariance. We assessed the intelligence, verbal fluency, processing speed, and executive functioning of the participants and further investigated the correlations of the GM structural networks with intelligence and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component and the frontal component were significantly associated with intelligence. The parietal and frontal regions were each distinctively associated with intelligence by maintaining structural networks with the cerebellum and the temporal region, respectively. The cerebellar component was associated with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by demonstrating how each core region for intelligence works in concert with other regions. In addition, we revealed how the cerebellum is associated with intelligence and cognitive functions.

Patterns of Co-Occurring Gray Matter Concentration Loss across the Huntington Disease Prodrome

Huntington disease (HD) is caused by an abnormally expanded cytosine-adenine-guanine (CAG) trinucleotide repeat in the HTT gene. Age and CAG-expansion number are related to age at diagnosis and can be used to index disease progression. However, observed onset-age variability suggests that other factors also modulate progression. Indexing prodromal (pre-diagnosis) progression may highlight therapeutic targets by isolating the earliest-affected factors. We present the largest prodromal HD application of the univariate method voxel-based morphometry (VBM) and the first application of the multivariate method source-based morphometry (SBM) to, respectively, compare gray matter concentration (GMC) and capture co-occurring GMC patterns in control and prodromal participants. Using structural MRI data from 1050 (831 prodromal, 219 control) participants, we characterize control-prodromal, whole-brain GMC differences at various prodromal stages. Our results provide evidence for (1) regional co-occurrence and differential patterns of decline across the prodrome, with parietal and occipital differences commonly co-occurring, and frontal and temporal differences being relatively independent from one another, (2) fronto-striatal circuits being among the earliest and most consistently affected in the prodrome, (3) delayed degradation in some movement-related regions, with increasing subcortical and occipital differences with later progression, (4) an overall superior-to-inferior gradient of GMC reduction in frontal, parietal, and temporal lobes, and (5) the appropriateness of SBM for studying the prodromal HD population and its enhanced sensitivity to early prodromal and regionally concurrent differences.

Uncovering the Social Deficits in the Autistic Brain. A Source-Based Morphometric Study

Autism is a neurodevelopmental disorder that mainly affects social interaction and communication. Evidence from behavioral and functional MRI studies supports the hypothesis that dysfunctional mechanisms involving social brain structures play a major role in autistic symptomatology. However, the investigation of anatomical abnormalities in the brain of people with autism has led to inconsistent results. We investigated whether specific brain regions, known to display functional abnormalities in autism, may exhibit mutual and peculiar patterns of covariance in their gray-matter concentrations. We analyzed structural MRI images of 32 young men affected by autistic disorder (AD) and 50 healthy controls. Controls were matched for sex, age, handedness. IQ scores were also monitored to avoid confounding. A multivariate Source-Based Morphometry (SBM) was applied for the first time on AD and controls to detect maximally independent networks of gray matter. Group comparison revealed a gray-matter source that showed differences in AD compared to controls. This network includes broad temporal regions involved in social cognition and high-level visual processing, but also motor and executive areas of the frontal lobe. Notably, we found that gray matter differences, as reflected by SBM, significantly correlated with social and behavioral deficits displayed by AD individuals and encoded via the Autism Diagnostic Observation Schedule scores. These findings provide support for current hypotheses about the neural basis of atypical social and mental states information processing in autism.

New Insights into the Nature of Cerebellar-Dependent Eyeblink Conditioning Deficits in Schizophrenia: A Hierarchical Linear Modeling Approach

Evidence of cerebellar dysfunction in schizophrenia has mounted over the past several decades, emerging from neuroimaging, neuropathological, and behavioral studies. Consistent with these findings, cerebellar-dependent delay eyeblink conditioning (dEBC) deficits have been identified in schizophrenia. While repeated-measures analysis of variance is traditionally used to analyze dEBC data, hierarchical linear modeling (HLM) more reliably describes change over time by accounting for the dependence in repeated-measures data. This analysis approach is well suited to dEBC data analysis because it has less restrictive assumptions and allows unequal variances. The current study examined dEBC measured with electromyography in a single-cue tone paradigm in an age-matched sample of schizophrenia participants and healthy controls (N = 56 per group) using HLM. Subjects participated in 90 trials (10 blocks) of dEBC, during which a 400 ms tone co-terminated with a 50 ms air puff delivered to the left eye. Each block also contained 1 tone-alone trial. The resulting block averages of dEBC data were fitted to a three-parameter logistic model in HLM, revealing significant differences between schizophrenia and control groups on asymptote and inflection point, but not slope. These findings suggest that while the learning rate is not significantly different compared to controls, associative learning begins to level off later and a lower ultimate level of associative learning is achieved in schizophrenia. Given the large sample size in the present study, HLM may provide a more nuanced and definitive analysis of differences between schizophrenia and controls on dEBC.

Genome-wide significant linkage of schizophrenia-related neuroanatomical trait to 12q24

The insula and medial prefrontal cortex (mPFC) share functional, histological, transcriptional, and developmental characteristics, and they serve higher cognitive functions of theoretical relevance to schizophrenia and related disorders. Meta-analyses and multivariate analysis of structural magnetic resonance imaging (MRI) scans indicate that gray matter density and volume reductions in schizophrenia are the most consistent and pronounced in a network primarily composed of the insula and mPFC. We used source-based morphometry, a multivariate technique optimized for structural MRI, in a large sample of randomly ascertained pedigrees (N = 887) to derive an insula-mPFC component and to investigate its genetic determinants. Firstly, we replicated the insula-mPFC gray matter component as an independent source of gray matter variation in the general population, and verified its relevance to schizophrenia in an independent case-control sample. Secondly, we showed that the neuroanatomical variation defined by this component is largely determined by additive genetic variation (h(2)  = 0.59), and genome-wide linkage analysis resulted in a significant linkage peak at 12q24 (LOD = 3.76). This region has been of significant interest to psychiatric genetics as it contains the Darier's disease locus and other proposed susceptibility genes (e.g., DAO, NOS1), and it has been linked to affective disorders and schizophrenia in multiple populations. Thus, in conjunction with previous clinical studies, our data imply that one or more psychiatric risk variants at 12q24 are co-inherited with reductions in mPFC and insula gray matter concentration. © 2015 Wiley Periodicals, Inc.

Structural correlates of formal thought disorder in schizophrenia: An ultra-high field multivariate morphometry study

BACKGROUND

Persistent formal thought disorder (FTD) is one of the most characteristic features of schizophrenia. Several neuroimaging studies report spatially distinct neuroanatomical changes in association with FTD. Given that most studies so far have employed a univariate localisation approach that obscures the study of covarying interregional relationships, the present study focussed on the multivariate systemic pattern of anatomical changes that contribute to FTD.

METHODS

Speech samples from nineteen medicated clinically stable schizophrenia patients and 20 healthy controls were evaluated for subtle formal thought disorder. Ultra high-field (7T) anatomical Magnetic Resonance Imaging scans were obtained from all subjects. Multivariate morphometric patterns were identified using an independent component approach (source based morphometry). Using multiple regression analysis, the morphometric patterns predicting positive and negative FTD scores were identified.

RESULTS

Morphometric variations in grey matter predicted a substantial portion of inter-individual variance in negative but not positive FTD. A pattern of concomitant striato-insular/precuneus reduction along with frontocingular grey matter increase had a significant association with negative FTD.

CONCLUSIONS

These results suggest that concomitant increase and decrease in grey matter occur in association with persistent negative thought disorder in clinically stable individuals with schizophrenia.

Alterations of Gray and White Matter Networks in Patients with Obsessive-Compulsive Disorder: A Multimodal Fusion Analysis of Structural MRI and DTI Using mCCA+jICA

Many of previous neuroimaging studies on neuronal structures in patients with obsessive-compulsive disorder (OCD) used univariate statistical tests on unimodal imaging measurements. Although the univariate methods revealed important aberrance of local morphometry in OCD patients, the covariance structure of the anatomical alterations remains unclear. Motivated by recent developments of multivariate techniques in the neuroimaging field, we applied a fusion method called "mCCA+jICA" on multimodal structural data of T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) of 30 unmedicated patients with OCD and 34 healthy controls. Amongst six highly correlated multimodal networks (p < 0.0001), we found significant alterations of the interrelated gray and white matter networks over occipital and parietal cortices, frontal interhemispheric connections and cerebella (False Discovery Rate q ≤ 0.05). In addition, we found white matter networks around basal ganglia that correlated with a subdimension of OC symptoms, namely 'harm/checking' (q ≤ 0.05). The present study not only agrees with the previous unimodal findings of OCD, but also quantifies the association of the altered networks across imaging modalities.

Brain differences in first-episode schizophrenia treated with quetiapine: a deformation-based morphometric study

RATIONALE

With the development of various imaging techniques, the deformation-based morphometry (DBM) method provides an objective automatic examination of the whole brain.

OBJECTIVES

This study aims to assess the abnormalities in the brains of first-episode schizophrenia (FES) patients treated with quetiapine using another advanced nonrigid registration method, hierarchical attribute matching mechanism for elastic registration, through the application of DBM in the entire brain.

METHODS

Thirty FES patients and 30 normal controls were grouped by age and handedness and subjected to magnetic resonance imaging examination. The patients had relatively short durations of untreated psychosis (DUP; 6.4 ± 5.2 months), and only a single antipsychotic drug, quetiapine (dosage, 200 ± 75 mg), was used for treatment. Statistically significant changes in regional volume were analyzed via DBM. In addition, a voxel-wise analysis of correlations between the duration of treatment or dosage and volume was also performed.

RESULTS

Compared with control subjects, FES patients showed contracted regions located in Brodmann area (BA) 42 and BA 19. By contrast, expanded regions were observed in BA 38, BA 21, BA 6 and 8, and left cerebellum. A negative correlation was observed between dosage and volume in the hippocampus, while a positive correlation was found in the caudate. Meanwhile, a negative correlation was observed between duration of treatment and volume in BA 38.

CONCLUSION

Both regional volume reductions and increases were detected in the brains of FES patients treated with quetiapine compared with healthy control subjects. Such differences may be partially relevant to dosage and treatment duration in clinic.

Eyeblink Conditioning in Schizophrenia: A Critical Review

There is accruing evidence of cerebellar abnormalities in schizophrenia. The theory of cognitive dysmetria considers cerebellar dysfunction a key component of schizophrenia. Delay eyeblink conditioning (EBC), a cerebellar-dependent translational probe, is a behavioral index of cerebellar integrity. The circuitry underlying EBC has been well characterized by non-human animal research, revealing the cerebellum as the essential circuitry for the associative learning instantiated by this task. However, there have been persistent inconsistencies in EBC findings in schizophrenia. This article thoroughly reviews published studies investigating EBC in schizophrenia, with an emphasis on possible effects of antipsychotic medication and stimulus and analysis parameters on reports of EBC performance in schizophrenia. Results indicate a consistent finding of impaired EBC performance in schizophrenia, as measured by decreased rates of conditioning, and that medication or study design confounds do not account for this impairment. Results are discussed within the context of theoretical and neurochemical models of schizophrenia.

Impaired cerebellar-dependent eyeblink conditioning in first-degree relatives of individuals with schizophrenia

Consistent with reports of cerebellar structural, functional, and neurochemical anomalies in schizophrenia, robust cerebellar-dependent delay eyeblink conditioning (dEBC) deficits have been observed in the disorder. Impaired dEBC is also present in schizotypal personality disorder, an intermediate phenotype of schizophrenia. The present work sought to determine whether dEBC deficits exist in nonpsychotic first-degree relatives of individuals with schizophrenia. A single-cue tone dEBC paradigm consisting of 10 blocks with 10 trials each (9 paired and 1 unpaired trials) was used to examine the functional integrity of cerebellar circuitry in schizophrenia participants, individuals with a first-degree relative diagnosed with schizophrenia, and healthy controls with no first-degree relatives diagnosed with schizophrenia. The conditioned stimulus (a 400ms tone) coterminated with the unconditioned stimulus (a 50ms air puff to the left eye) on paired trials. One relative and 2 healthy controls were removed from further analysis due to declining conditioned response rates, leaving 18 schizophrenia participants, 17 first-degree relatives, and 16 healthy controls. Electromyographic data were subsequently analyzed using growth curve models in hierarchical linear regression. Acquisition of dEBC conditioned responses was significantly impaired in schizophrenia and first-degree relative groups compared with controls. This finding that cerebellar-mediated associative learning deficits are present in first-degree relatives of individuals with schizophrenia provides evidence that dEBC abnormalities in schizophrenia may not be due to medication or course of illness effects. Instead, the present results are consistent with models of schizophrenia positing cerebellar-cortical circuit abnormalities and suggest that cerebellar abnormalities represent a risk marker for the disorder.

Increased timing variability in schizophrenia and bipolar disorder

Theoretical and empirical evidence suggests that impaired time perception and the neural circuitry underlying internal timing mechanisms may contribute to severe psychiatric disorders, including psychotic and mood disorders. The degree to which alterations in temporal perceptions reflect deficits that exist across psychosis-related phenotypes and the extent to which mood symptoms contribute to these deficits is currently unknown. In addition, compared to schizophrenia, where timing deficits have been more extensively investigated, sub-second timing has been studied relatively infrequently in bipolar disorder. The present study compared sub-second duration estimates of schizophrenia (SZ), schizoaffective disorder (SA), non-psychotic bipolar disorder (BDNP), bipolar disorder with psychotic features (BDP), and healthy non-psychiatric controls (HC) on a well-established time perception task using sub-second durations. Participants included 66 SZ, 37 BDNP, 34 BDP, 31 SA, and 73 HC who participated in a temporal bisection task that required temporal judgements about auditory durations ranging from 300 to 600 milliseconds. Timing variability was significantly higher in SZ, BDP, and BDNP groups compared to healthy controls. The bisection point did not differ across groups. These findings suggest that both psychotic and mood symptoms may be associated with disruptions in internal timing mechanisms. Yet unexpected findings emerged. Specifically, the BDNP group had significantly increased variability compared to controls, but the SA group did not. In addition, these deficits appeared to exist independent of current symptom status. The absence of between group differences in bisection point suggests that increased variability in the SZ and bipolar disorder groups are due to alterations in perceptual timing in the sub-second range, possibly mediated by the cerebellum, rather than cognitive deficits.

Grey matter changes in cognitively impaired Parkinson's disease patients

Background

Cortical changes associated with cognitive decline in Parkinson's disease (PD) are not fully explored and require investigations with established diagnostic classification criteria.

Objective

We used MRI source-based morphometry to evaluate specific differences in grey matter volume patterns across 4 groups of subjects: healthy controls (HC), PD with normal cognition (PD-NC), PD with mild cognitive impairment (MCI-PD) and PD with dementia (PDD).

Methods

We examined 151 consecutive subjects: 25 HC, 75 PD-NC, 29 MCI-PD, and 22 PDD at an Italian and Czech movement disorder centre. Operational diagnostic criteria were applied to classify MCI-PD and PDD. All structural MRI images were processed together in the Czech centre. The spatial independent component analysis was used to assess group differences of local grey matter volume.

Results

We identified two independent patterns of grey matter volume deviations: a) Reductions in the hippocampus and temporal lobes; b) Decreases in fronto-parietal regions and increases in the midbrain/cerebellum. Both patterns differentiated PDD from all other groups and correlated with visuospatial deficits and letter verbal fluency, respectively. Only the second pattern additionally differentiated PD-NC from HC.

Conclusion

Grey matter changes in PDD involve areas associated with Alzheimer-like pathology while fronto-parietal abnormalities are possibly an early marker of PD cognitive decline. These findings are consistent with a non-linear cognitive progression in PD.

Structural interactions within the default mode network identified by Bayesian network analysis in Alzheimer's disease

Alzheimer’s disease (AD) is a well-known neurodegenerative disease that is associated with dramatic morphological abnormalities. The default mode network (DMN) is one of the most frequently studied resting-state networks. However, less is known about specific structural dependency or interactions among brain regions within the DMN in AD. In this study, we performed a Bayesian network (BN) analysis based on regional grey matter volumes to identify differences in structural interactions among core DMN regions in structural MRI data from 80 AD patients and 101 normal controls (NC). Compared to NC, the structural interactions between the medial prefrontal cortex (mPFC) and other brain regions, including the left inferior parietal cortex (IPC), the left inferior temporal cortex (ITC) and the right hippocampus (HP), were significantly reduced in the AD group. In addition, the AD group showed prominent increases in structural interactions from the left ITC to the left HP, the left HP to the right ITC, the right HP to the right ITC, and the right IPC to the posterior cingulate cortex (PCC). The BN models significantly distinguished AD patients from NC with 87.12% specificity and 81.25% sensitivity. We then used the derived BN models to examine the replicability and stability of AD-associated BN models in an independent dataset and the results indicated discriminability with 83.64% specificity and 80.49% sensitivity. The results revealed that the BN analysis was effective for characterising regional structure interactions and the AD-related BN models could be considered as valid and predictive structural brain biomarker models for AD. Therefore, our study can assist in further understanding the pathological mechanism of AD, based on the view of the structural network, and may provide new insights into classification and clinical application in the study of AD in the future.

Independent component analysis of DTI reveals multivariate microstructural correlations of white matter in the human brain

It has recently been demonstrated that specific patterns of correlation exist in diffusion tensor imaging (DTI) parameters across white matter tracts in the normal human brain. These microstructural correlations are thought to reflect phylogenetic and functional similarities between different axonal fiber pathways. However, this earlier work was limited in three major respects: (1) the analysis was restricted to only a dozen selected tracts; (2) the DTI measurements were averaged across whole tracts, whereas metrics such as fractional anisotropy (FA) are known to vary considerably within single tracts; and (3) a univariate measure of correlation was used. In this investigation, we perform an automated multivariate whole-brain voxel-based study of white matter FA correlations using independent component analysis (ICA) of tract-based spatial statistics computed from 3T DTI in 53 healthy adult volunteers. The resulting spatial maps of the independent components show voxels for which the FA values within each map co-vary across individuals. The strongest FA correlations were found in anatomically recognizable tracts and tract segments, either singly or in homologous pairs. Hence, ICA of DTI provides an automated unsupervised decomposition of the normal human brain into multiple separable microstructurally correlated white matter regions, many of which correspond to anatomically familiar classes of white matter pathways. Further research is needed to determine whether whole-brain ICA of DTI represents a novel alternative to tractography for feature extraction in studying the normal microstructure of human white matter as well as the abnormal white matter microstructure found in neurological and psychiatric disorders.

Are Bipolar Disorder and Schizophrenia Neuroanatomically Distinct? An Anatomical Likelihood Meta-analysis

Objective: There is renewed debate on whether modern diagnostic classification should adopt a dichotomous or dimensional approach to schizophrenia and bipolar disorder. This study synthesizes data from voxel-based studies of schizophrenia and bipolar disorder to estimate the extent to which these conditions have a common neuroanatomical phenotype. Methods: A post-hoc meta-analytic estimation of the extent to which bipolar disorder, schizophrenia, or both conditions contribute to brain gray matter differences compared to controls was achieved using a novel application of the conventional anatomical likelihood estimation (ALE) method. 19 schizophrenia studies (651 patients and 693 controls) were matched as closely as possible to 19 bipolar studies (540 patients and 745 controls). Result: Substantial overlaps in the regions affected by schizophrenia and bipolar disorder included regions in prefrontal cortex, thalamus, left caudate, left medial temporal lobe, and right insula. Bipolar disorder and schizophrenia jointly contributed to clusters in the right hemisphere, but schizophrenia was almost exclusively associated with additional gray matter deficits (left insula and amygdala) in the left hemisphere. Limitation: The current meta-analytic method has a number of constraints. Importantly, only studies identifying differences between controls and patient groups could be included in this analysis. Conclusion: Bipolar disorder shares many of the same brain regions as schizophrenia. However, relative to neurotypical controls, lower gray matter volume in schizophrenia is more extensive and includes the amygdala. This fresh application of ALE accommodates multiple studies in a relatively unbiased comparison. Common biological mechanisms may explain the neuroanatomical overlap between these major disorders, but explaining why brain differences are more extensive in schizophrenia remains challenging.