Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia

Autoconnectivity: A new perspective on human brain function

BACKGROUND

Autocorrelation (AC) in fMRI time-series is a well-known phenomenon, typically attributed to colored noise and therefore removed from the data. We hypothesize that AC reflects systematic and meaningful signal fluctuations that may be tied to neural activity and provide evidence to support this hypothesis.

NEW METHOD

Each fMRI time-series is modeled as an autoregressive process from which the autocorrelation is quantified. Then, autocorrelation during resting-state fMRI and auditory oddball (AOD) task in schizophrenia and healthy volunteers is examined.

RESULTS

During resting-state, AC was higher in the visual cortex while during AOD task, frontal part of the brain exhibited higher AC in both groups. AC values were significantly lower in specific brain regions in schizophrenia patients (such as thalamus during resting-state) compared to healthy controls in two independent datasets. Moreover, AC values had significant negative correlation with patients' symptoms. AC differences discriminated patients from healthy controls with high accuracy (resting-state).

COMPARISON WITH EXISTING METHODS

Contrary to most prior works, the results suggest AC shows meaningful patterns that are discriminative between patients and controls. Our results are in line with recent works attributing autocorrelation to feedback loop of brain's regulatory circuit.

CONCLUSIONS

Autoconnectivity is cognitive state dependent (resting-state vs. task) and mental state dependent (healthy vs. schizophrenia). The concept of autoconnectivity resembles a recurrent neural network and provides a new perspective of functional integration in the brain. These findings may have important implications for understanding of brain function in health and disease as well as for analysis of fMRI time-series.

Altered functional connectivity between sub-regions in the thalamus and cortex in schizophrenia patients measured by resting state BOLD fMRI at 7T

The thalamus is a small brain structure that relays neuronal signals between subcortical and cortical regions. Abnormal thalamocortical connectivity in schizophrenia has been reported in previous studies using blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI) performed at 3T. However, anatomically the thalamus is not a single entity, but is subdivided into multiple distinct nuclei with different connections to various cortical regions. We sought to determine the potential benefit of using the enhanced sensitivity of BOLD fMRI at ultra-high magnetic field (7T) in exploring thalamo-cortical connectivity in schizophrenia based on subregions in the thalamus. Seeds placed in thalamic subregions of 14 patients and 14 matched controls were used to calculate whole-brain functional connectivity. Our results demonstrate impaired thalamic connectivity to the prefrontal cortex and the cerebellum, but enhanced thalamic connectivity to the motor/sensory cortex in schizophrenia. This altered functional connectivity significantly correlated with disease duration in the patients. Remarkably, comparable effect sizes observed in previous 3T studies were detected in the current 7T study with a heterogeneous and much smaller cohort, providing evidence that ultra-high field fMRI may be a powerful tool for measuring functional connectivity abnormalities in schizophrenia. Further investigation with a larger cohort is merited to validate the current findings.

Disconnected and Hyperactive: A Replication of Sensorimotor Cortex Abnormalities in Patients With Schizophrenia During Proactive Response Inhibition

Inhibitory failure represents a core dysfunction in patients with schizophrenia (SP), which has predominantly been tested in the literature using reactive (ie, altering behavior after a stimulus) rather than proactive (ie, purposefully changing behavior before a stimulus) response inhibition tasks. The current study replicates/extends our previous findings of SP exhibiting sensorimotor cortex (SMC) hyperactivity and connectivity abnormalities in independent samples of patients and controls. Specifically, 49 clinically well-characterized SP and 54 matched healthy controls (HC) performed a proactive response inhibition task while undergoing functional magnetic resonance imaging and resting-state data collection. Results indicated that the majority of SP (84%) and HC (88%) successfully inhibited all overt motor responses following a cue, eliminating behavioral confounds frequently present in this population. Observations of left SMC hyperactivity during proactive response inhibition, reduced cortical connectivity with left SMC, and increased connectivity between left SMC and ventrolateral thalamus were replicated for SP relative to HC in the current study. Similarly, negative symptoms (eg, motor retardation) were again associated with SMC functional and connectivity abnormalities. In contrast, findings of a negative blood oxygenation level-dependent response in the SMC of HC did not replicate. Collectively, current and previous findings suggest that SMC connectivity abnormalities may be more robust relative to evoked hemodynamic signals during proactive response inhibition. In addition, there is strong support that these SMC abnormalities are a key component of SP pathology, along with dysfunction within other sensory cortices, and may be associated with certain clinical deficits such as negative symptoms.

Gray Matter Microstructural Abnormalities and Working Memory Deficits in Individuals with Schizophrenia

OBJECTIVE

Working memory impairments serve as prognostic factors for patients with schizophrenia. Working memory deficits are mainly associated with gray matter (GM) thickness and volume. We investigated the association between GM diffusivity and working memory in controls and individuals with schizophrenia.

METHODS

T1 and diffusion tensor images of the brain, working memory task (letter number sequencing) scores, and the demographic data of 90 individuals with schizophrenia and 97 controls were collected from the SchizConnect database. T1 images were parcellated into the 68 GM Regions of Interest (ROI). Axial Diffusivity (AD), Fractional Anisotropy (FA), Radial Diffusivity (RD), and Trace (TR) were calculated for each of the ROIs.

RESULTS

Compared to the controls, schizophrenia group showed significantly increased AD, RD, and TR in specific regions on the frontal, temporal, and anterior cingulate area. Moreover, working memory was negatively correlated with AD, RD, and TR in the lateral orbitofrontal, superior temporal, inferior temporal, and rostral anterior cingulate area on left hemisphere in the individuals with schizophrenia.

CONCLUSION

These results demonstrated GM microstructural abnormalities in the frontal, temporal, and anterior cingulate regions of individuals with schizophrenia. Furthermore, these regional GM microstructural abnormalities suggest a neuropathological basis for the working memory deficits observed clinically in individuals with schizophrenia.

Resting-state anticorrelated networks in Schizophrenia

Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under explored. Hence, in this study we examined (1) the resting-state anticorrelated networks in schizophrenia, and (2) the accuracy of support vector machines (SVMs) in differentiating healthy individuals from schizophrenia patients using these anticorrelated networks. The sample consisted of 56 patients with DSM-IV schizophrenia and 56 healthy controls. We computed functional connectivity matrices and used Anticorrelation after Mean of Antilog method (AMA) to select predominantly anticorrelated networks. The basal ganglia, thalamus, lingual gyrus, and cerebellar vermis showed significantly different, Type A (decreased anticorrelation) connections. The medial temporal lobe and posterior cingulate gyri showed significantly different, Type B (increased anticorrelation) connections. Use of SVM on AMA networks showed moderate accuracy in differentiating schizophrenia and healthy controls. Our results suggest that anticorrelated networks between the sub-cortical and cortical areas are abnormal in schizophrenia and this has potential to be a differential biomarker. These preliminary findings, if replicated in future studies with larger number of patients, and advanced machine learning techniques could have potential clinical applications.

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

Resting-state thalamic dysconnectivity in schizophrenia and relationships with symptoms

BACKGROUND

Schizophrenia (SZ) is a severe neuropsychiatric disorder associated with disrupted connectivity within the thalamic-cortico-cerebellar network. Resting-state functional connectivity studies have reported thalamic hypoconnectivity with the cerebellum and prefrontal cortex as well as thalamic hyperconnectivity with sensory cortical regions in SZ patients compared with healthy comparison participants (HCs). However, fundamental questions remain regarding the clinical significance of these connectivity abnormalities.

METHOD

Resting state seed-based functional connectivity was used to investigate thalamus to whole brain connectivity using multi-site data including 183 SZ patients and 178 matched HCs. Statistical significance was based on a voxel-level FWE-corrected height threshold of p < 0.001. The relationships between positive and negative symptoms of SZ and regions of the brain demonstrating group differences in thalamic connectivity were examined.

RESULTS

HC and SZ participants both demonstrated widespread positive connectivity between the thalamus and cortical regions. Compared with HCs, SZ patients had reduced thalamic connectivity with bilateral cerebellum and anterior cingulate cortex. In contrast, SZ patients had greater thalamic connectivity with multiple sensory-motor regions, including bilateral pre- and post-central gyrus, middle/inferior occipital gyrus, and middle/superior temporal gyrus. Thalamus to middle temporal gyrus connectivity was positively correlated with hallucinations and delusions, while thalamus to cerebellar connectivity was negatively correlated with delusions and bizarre behavior.

CONCLUSIONS

Thalamic hyperconnectivity with sensory regions and hypoconnectivity with cerebellar regions in combination with their relationship to clinical features of SZ suggest that thalamic dysconnectivity may be a core neurobiological feature of SZ that underpins positive symptoms.

Altered spatial and temporal concordance among intrinsic brain activity measures in schizophrenia

Various data-driven voxel-wise measures derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been developed to characterize spontaneous brain activity. These measures have been widely applied to explore brain functional changes in schizophrenia and have enjoyed significant success in unraveling the neural mechanisms of this disorder. However, their spatial and temporal coupling alterations in schizophrenia remain largely unknown. To address this issue, 88 schizophrenia patients and 116 gender- and age-matched healthy controls underwent rs-fMRI examinations. Kendall's W was used to calculate volume-wise (across voxels) and voxel-wise (across time windows) concordance among multiple commonly used measures, including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity. Inter-group differences in the concordance were investigated. Results revealed that whole gray matter volume-wise concordance was reduced in schizophrenia patients relative to healthy controls. Although two groups showed similar spatial distributions of the voxel-wise concordance, quantitative comparison analysis revealed that schizophrenia patients exhibited decreased voxel-wise concordance in gray matter areas spanning the bilateral frontal, parietal, occipital, temporal and insular cortices. In addition, these concordance changes were negatively correlated with onset age in schizophrenia patients. Our findings suggest that the concordance approaches may provide new insights into the neural mechanisms of schizophrenia and have the potential to be extended to neuropsychiatric disorders.

An approach to directly link ICA and seed-based functional connectivity: Application to schizophrenia

Independent component analysis (ICA) and seed-based analyses are widely used techniques for studying intrinsic neuronal activity in task-based or resting scans. In this work, we show there is a direct link between the two, and show that there are some important differences between the two approaches in terms of what information they capture. We developed an enhanced connectivity-matrix independent component analysis (cmICA) for calculating whole brain voxel maps of functional connectivity, which reduces the computational complexity of voxel-based connectivity analysis on performing many temporal correlations. We also show there is a mathematical equivalency between parcellations on voxel-to-voxel functional connectivity and simplified cmICA. Next, we used this cost-efficient data-driven method to examine the resting state fMRI connectivity in schizophrenia patients (SZ) and healthy controls (HC) on a whole brain scale and further quantified the relationship between brain functional connectivity and cognitive performances measured by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) battery. Current results suggest that SZ exhibit a wide-range abnormality, primarily a decrease, in functional connectivity both between networks and within different network hubs. Specific functional connectivity decreases were associated with MATRICS performance deficits. In addition, we found that resting state functional connectivity decreases was extensively associated with aging regardless of groups. In contrast, there was no relationship between positive and negative symptoms in the patients and functional connectivity. In sum, we have developed a novel mathematical relationship between ICA and seed-based connectivity that reduces computational complexity, which has broad applicability, and showed a specific application of this approach to characterize connectivity changes associated with cognitive scores in SZ.

The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics

Data-driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data-driven methods that are based on two different forms of diversity-statistical properties of the data-statistical independence for ICA and sparsity for DL. Despite their popularity, the comparative advantage of emphasizing one property over another in the decomposition of fMRI data is not well understood. Such a comparison is made harder due to the differences in the modeling assumptions between ICA and DL, as well as within different ICA algorithms where each algorithm exploits a different form of diversity. In this paper, we propose the use of objective global measures, such as time course frequency power ratio, network connection summary, and graph theoretical metrics, to gain insight into the role that different types of diversity have on the analysis of fMRI data. Four ICA algorithms that account for different types of diversity and one DL algorithm are studied. We apply these algorithms to real fMRI data collected from patients with schizophrenia and healthy controls. Our results suggest that no one particular method has the best performance using all metrics, implying that the optimal method will change depending on the goal of the analysis. However, we note that in none of the scenarios we test the highly popular Infomax provides the best performance, demonstrating the cost of exploiting limited form of diversity.

TractSeg - Fast and accurate white matter tract segmentation

The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines is a unique combination of tools which enables the in-vivo delineation and analysis of anatomically well-known tracts. This, however, currently requires complex, computationally intensive processing pipelines which take a lot of time to set up. TractSeg is a novel convolutional neural network-based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without using tractography, image registration or parcellation. We demonstrate that the proposed approach is much faster than existing methods while providing unprecedented accuracy, using a population of 105 subjects from the Human Connectome Project. We also show initial evidence that TractSeg is able to generalize to differently acquired data sets for most of the bundles. The code and data are openly available at https://github.com/MIC-DKFZ/TractSeg/ and https://doi.org/10.5281/zenodo.1088277, respectively.

Multimodal Neuroimaging in Schizophrenia: Description and Dissemination

In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network's collaborative informatics and neuroimaging suite (COINS).

Aberrant functional network connectivity in psychopathy from a large (N = 985) forensic sample

Psychopathy is a personality disorder characterized by antisocial behavior, lack of remorse and empathy, and impaired decision making. The disproportionate amount of crime committed by psychopaths has severe emotional and economic impacts on society. Here we examine the neural correlates associated with psychopathy to improve early assessment and perhaps inform treatments for this condition. Previous resting-state functional magnetic resonance imaging (fMRI) studies in psychopathy have primarily focused on regions of interest. This study examines whole-brain functional connectivity and its association to psychopathic traits. Psychopathy was hypothesized to be characterized by aberrant functional network connectivity (FNC) in several limbic/paralimbic networks. Group-independent component and regression analyses were applied to a data set of resting-state fMRI from 985 incarcerated adult males. We identified resting-state networks (RSNs), estimated FNC between RSNs, and tested their association to psychopathy factors and total summary scores (Factor 1, interpersonal/affective; Factor 2, lifestyle/antisocial). Factor 1 scores showed both increased and reduced functional connectivity between RSNs from seven brain domains (sensorimotor, cerebellar, visual, salience, default mode, executive control, and attentional). Consistent with hypotheses, RSNs from the paralimbic system-insula, anterior and posterior cingulate cortex, amygdala, orbital frontal cortex, and superior temporal gyrus-were related to Factor 1 scores. No significant FNC associations were found with Factor 2 and total PCL-R scores. In summary, results suggest that the affective and interpersonal symptoms of psychopathy (Factor 1) are associated with aberrant connectivity in multiple brain networks, including paralimbic regions.

Decreased hemispheric connectivity and decreased intra- and inter- hemisphere asymmetry of resting state functional network connectivity in schizophrenia

Many studies have shown that schizophrenia patients have aberrant functional network connectivity (FNC) among brain regions, suggesting schizophrenia manifests with significantly diminished (in majority of the cases) connectivity. Schizophrenia is also associated with a lack of hemispheric lateralization. Hoptman et al. (2012) reported lower inter-hemispheric connectivity in schizophrenia patients compared to controls using voxel-mirrored homotopic connectivity. In this study, we merge these two points of views together using a group independent component analysis (gICA)-based approach to generate hemisphere-specific timecourses and calculate intra-hemisphere and inter-hemisphere FNC on a resting state fMRI dataset consisting of age- and gender-balanced 151 schizophrenia patients and 163 healthy controls. We analyzed the group differences between patients and healthy controls in each type of FNC measures along with age and gender effects. The results reveal that FNC in schizophrenia patients shows less hemispheric asymmetry compared to that of the healthy controls. We also found a decrease in connectivity in all FNC types such as intra-left (L_FNC), intra-right (R_FNC) and inter-hemisphere (Inter_FNC) in the schizophrenia patients relative to healthy controls, but general patterns of connectivity were preserved in patients. Analyses of age and gender effects yielded results similar to those reported in whole brain FNC studies.

A Longitudinal Mapping Study on Cortical Plasticity of Peripheral Nerve Injury Treated by Direct Anastomosis and Electroacupuncture in Rats

OBJECTIVE

We used functional magnetic resonance imaging to provide a longitudinal description of cortical plasticity caused by electroacupuncture (EA) of sciatic nerve transection and direct anastomosis in rats.

METHODS

Sixteen rats in a sciatic nerve transection and direct anastomosis model were randomly divided into intervention and control groups. EA intervention in the position of ST-36, GB-30 was conducted continuously for 4 months in the intervention group. Functional magnetic resonance imaging and gait assessment were performed every month after intervention.

RESULTS

The somatosensory area was more activated in the first 2 months and then deactivated in the rest 2 months when EA was applied. The pain-related areas had the same activation pattern as the somatosensory area. The limbic/paralimbic areas fluctuated more during the EA intervention, which was not constantly activated or deactivated as previous studies reported. We attributed such changes in somatosensory and pain-related areas to the gradual reduction of sensory afferentation. The alterations in limbic/paralimbic system might be associated with the confrontation between the upregulating effect of paresthesia or pain and the downregulating effect of EA intervention through the autonomic nerve system. The gait analysis showed significantly higher maximum contact mean intensity in the intervention group.

CONCLUSIONS

The alterations in the brain brought about by the long-term therapeutic effect of EA could be described as a synchronized activation pattern in the somatosensory and pain-related areas and a fluctuating pattern in the limbic/paralimbic system.

Disrupted modular organization of primary sensory brain areas in schizophrenia

Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by schizophrenia (SCZ) using functional MRI and other neuroimaging techniques. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the organization of brain connectivity networks. A few studies have shown altered distribution of connectivity within and between functional modules in SCZ patients, an indication of imbalanced functional segregation ad integration. However, no major alterations of modular organization have been reported in patients, and unambiguous identification of the neural substrates affected remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe resolution limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This resolution limit is likely to have hampered the ability to resolve differences between patients and controls in previous studies. Here, we apply Surprise, a novel resolution limit-free approach, to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients and in matched healthy controls. Leveraging these important methodological advances we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings support the hypothesis that cognitive dysfunction in SCZ may involve deficits occurring already at early stages of sensory processing.

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain

OBJECTIVE

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features.

MATERIALS AND METHODS

T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ.

RESULTS

The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005).

CONCLUSION

A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity

Our objective was to assess the generalizability, across sites and cognitive contexts, of schizophrenia classification based on functional brain connectivity. We tested different training-test scenarios combining fMRI data from 191 schizophrenia patients and 191 matched healthy controls obtained at 6 scanning sites and under different task conditions. Diagnosis classification accuracy generalized well to a novel site and cognitive context provided data from multiple sites were used for classifier training. By contrast, lower classification accuracy was achieved when data from a single distinct site was used for training. These findings indicate that it is beneficial to use multisite data to train fMRI-based classifiers intended for large-scale use in the clinical realm.

Response to Targeted Cognitive Training Correlates with Change in Thalamic Volume in a Randomized Trial for Early Schizophrenia

Reduced thalamic volume is consistently observed in schizophrenia, and correlates with cognitive impairment. Targeted cognitive training (TCT) of auditory processing in schizophrenia drives improvements in cognition that are believed to result from functional neuroplasticity in prefrontal and auditory cortices. In this study, we sought to determine whether response to TCT is also associated with structural neuroplastic changes in thalamic volume in patients with early schizophrenia (ESZ). Additionally, we examined baseline clinical, cognitive, and neural characteristics predictive of a positive response to TCT. ESZ patients were randomly assigned to undergo either 40 h of TCT (N=22) or a computer games control condition (CG; N=22 s). Participants underwent MRI, clinical, and neurocognitive assessments before and after training (4-month interval). Freesurfer automated segmentation of the subcortical surface was carried out to measure thalamic volume at both time points. Left thalamic volume at baseline correlated with baseline global cognition, while a similar trend was observed in the right thalamus. The relationship between change in cognition and change in left thalamus volume differed between groups, with a significant positive correlation in the TCT group and a negative trend in the CG group. Lower baseline symptoms were related to improvements in cognition and left thalamic volume preservation following TCT. These findings suggest that the cognitive gains induced by TCT in ESZ are associated with structural neuroplasticity in the thalamus. Greater symptom severity at baseline reduced the likelihood of response to TCT both with respect to improved cognition and change in thalamic volume.

A case for motor network contributions to schizophrenia symptoms: Evidence from resting-state connectivity

Though schizophrenia (SCZ) is classically defined based on positive symptoms and the negative symptoms of the disease prove to be debilitating for many patients, motor deficits are often present as well. A growing literature highlights the importance of motor systems and networks in the disease, and it may be the case that dysfunction in motor networks relates to the pathophysiology and etiology of SCZ. To test this and build upon recent work in SCZ and in at-risk populations, we investigated cortical and cerebellar motor functional networks at rest in SCZ and controls using publically available data. We analyzed data from 82 patients and 88 controls. We found key group differences in resting-state connectivity patterns that highlight dysfunction in motor circuits and also implicate the thalamus. Furthermore, we demonstrated that in SCZ, these resting-state networks are related to both positive and negative symptom severity. Though the ventral prefrontal cortex and corticostriatal pathways more broadly have been implicated in negative symptom severity, here we extend these findings to include motor-striatal connections, as increased connectivity between the primary motor cortex and basal ganglia was associated with more severe negative symptoms. Together, these findings implicate motor networks in the symptomatology of psychosis, and we speculate that these networks may be contributing to the etiology of the disease. Overt motor deficits in SCZ may signal underlying network dysfunction that contributes to the overall disease state. Hum Brain Mapp 38:4535-4545, 2017. © 2017 Wiley Periodicals, Inc.

Disrupted Olfactory Integration in Schizophrenia: Functional Connectivity Study

BACKGROUND

Evidence for olfactory dysfunction in schizophrenia has been firmly established. However, in the typical understanding of schizophrenia, olfaction is not recognized to contribute to or interact with the illness. Despite the solid presence of olfactory dysfunction in schizophrenia, its relation to the rest of the illness remains largely unclear. Here, we aimed to examine functional connectivity of the olfactory bulb, olfactory tract, and piriform cortices and isolate the network that would account for the altered olfaction in schizophrenia.

METHODS

We examined the functional connectivity of these specific olfactory regions in order to isolate other brain regions associated with olfactory processing in schizophrenia. Using the resting state functional MRI data from the Center for Biomedical Research Excellence in Brain Function and Mental Illness, we compared 84 patients of schizophrenia and 90 individuals without schizophrenia.

RESULTS

The schizophrenia group showed disconnectivity between the anterior piriform cortex and the nucleus accumbens, between the posterior piriform cortex and the middle frontal gyrus, and between the olfactory tract and the visual cortices.

CONCLUSIONS

The current results suggest functional disconnectivity of olfactory regions in schizophrenia, which may account for olfactory dysfunction and disrupted integration with other sensory modalities in schizophrenia.

Abnormal Grey Matter Arteriolar Cerebral Blood Volume in Schizophrenia Measured With 3D Inflow-Based Vascular-Space-Occupancy MRI at 7T

Metabolic dysfunction and microvascular abnormality may contribute to the pathogenesis of schizophrenia. Most previous studies of cerebral perfusion in schizophrenia measured total cerebral blood volume (CBV) and cerebral blood flow (CBF) in the brain, which reflect the ensemble signal from the arteriolar, capillary, and venular compartments of the microvasculature. As the arterioles are the most actively regulated blood vessels among these compartments, they may be the most sensitive component of the microvasculature to metabolic disturbances. In this study, we adopted the inflow-based vascular-space-occupancy (iVASO) MRI approach to investigate alterations in the volume of small arterial (pial) and arteriolar vessels (arteriolar cerebral blood volume [CBVa]) in the brain of schizophrenia patients. The iVASO approach was extended to 3-dimensional (3D) whole brain coverage, and CBVa was measured in the brains of 12 schizophrenia patients and 12 matched controls at ultra-high magnetic field (7T). Significant reduction in grey matter (GM) CBVa was found in multiple areas across the whole brain in patients (relative changes of 14%-51% and effect sizes of 0.7-2.3). GM CBVa values in several regions in the temporal cortex showed significant negative correlations with disease duration in patients. GM CBVa increase was also found in a few brain regions. Our results imply that microvascular abnormality may play a role in schizophrenia, and suggest GM CBVa as a potential marker for the disease. Further investigation is needed to elucidate whether such effects are due to primary vascular impairment or secondary to other causes, such as metabolic dysfunction.

Global brain asymmetry is increased in schizophrenia and related to avolition

OBJECTIVE

Schizophrenia may be the result of a failure of the normal lateralization process of the brain. However, whole-brain asymmetry has not been assessed up to date. Here, we propose a novel measure of global brain asymmetry based on the Dice coefficient to quantify similarity between brain hemispheres.

METHOD

Global gray and white matter asymmetry was calculated from high-resolution T1 structural images acquired from 24 patients with schizophrenia and 26 healthy controls, age- and sex-matched. Some of the analyses were replicated in a much larger sample (n = 759) obtained from open-access online databases.

RESULTS

Patients with schizophrenia had more global gray matter asymmetry than controls. Additionally, increased gray matter asymmetry was associated with avolition, whereas the inverse relationship was found for anxiety at a trend level. These analyses were replicated in a larger sample and confirmed previous results.

CONCLUSION

Our findings suggest that global gray matter asymmetry is related to the concept of developmental stability and is a useful indicator of perturbations during neurodevelopment.

Functional connectivity when detecting rare visual targets in schizophrenia

Individuals with schizophrenia demonstrate difficulties in attending to important stimuli (e.g., targets) and ignoring distractors (e.g., non-targets). We used a visual oddball task during fMRI to examine functional connectivity within and between the ventral and dorsal attention networks to determine the relative contribution of each network to detection of rare visual targets in schizophrenia. The sample comprised 25 schizophrenia patients and 27 healthy controls. Psychophysiological interaction analysis was used to examine whole-brain functional connectivity in response to targets. We used the right temporo parietal junction (TPJ) as the seed region for the ventral network and the right medial intraparietal sulcus (IPS) as the seed region for the dorsal network. We found that connectivity between right IPS and right anterior insula (AI; a component of the ventral network) was significantly greater in controls than patients. Expected patterns of within- and between-network connectivity for right TPJ were observed in controls, and not significantly different in patients. These findings indicate functional connectivity deficits between the dorsal and ventral attention networks in schizophrenia that may create problems in processing relevant versus irrelevant stimuli. Understanding the nature of network disruptions underlying cognitive deficits of schizophrenia may help shed light on the pathophysiology of this disorder.

Multimodal MR-imaging reveals large-scale structural and functional connectivity changes in profound early blindness

In the setting of profound ocular blindness, numerous lines of evidence demonstrate the existence of dramatic anatomical and functional changes within the brain. However, previous studies based on a variety of distinct measures have often provided inconsistent findings. To help reconcile this issue, we used a multimodal magnetic resonance (MR)-based imaging approach to provide complementary structural and functional information regarding this neuroplastic reorganization. This included gray matter structural morphometry, high angular resolution diffusion imaging (HARDI) of white matter connectivity and integrity, and resting state functional connectivity MRI (rsfcMRI) analysis. When comparing the brains of early blind individuals to sighted controls, we found evidence of co-occurring decreases in cortical volume and cortical thickness within visual processing areas of the occipital and temporal cortices respectively. Increases in cortical volume in the early blind were evident within regions of parietal cortex. Investigating white matter connections using HARDI revealed patterns of increased and decreased connectivity when comparing both groups. In the blind, increased white matter connectivity (indexed by increased fiber number) was predominantly left-lateralized, including between frontal and temporal areas implicated with language processing. Decreases in structural connectivity were evident involving frontal and somatosensory regions as well as between occipital and cingulate cortices. Differences in white matter integrity (as indexed by quantitative anisotropy, or QA) were also in general agreement with observed pattern changes in the number of white matter fibers. Analysis of resting state sequences showed evidence of both increased and decreased functional connectivity in the blind compared to sighted controls. Specifically, increased connectivity was evident between temporal and inferior frontal areas. Decreases in functional connectivity were observed between occipital and frontal and somatosensory-motor areas and between temporal (mainly fusiform and parahippocampus) and parietal, frontal, and other temporal areas. Correlations in white matter connectivity and functional connectivity observed between early blind and sighted controls showed an overall high degree of association. However, comparing the relative changes in white matter and functional connectivity between early blind and sighted controls did not show a significant correlation. In summary, these findings provide complimentary evidence, as well as highlight potential contradictions, regarding the nature of regional and large scale neuroplastic reorganization resulting from early onset blindness.

Thalamo-cortical communication, glutamatergic neurotransmission and neural oscillations: A unique window into the origins of ScZ?

The thalamus has recently received renewed interest in systems-neuroscience and schizophrenia (ScZ) research because of emerging evidence highlighting its important role in coordinating functional interactions in cortical-subcortical circuits. Moreover, higher cognitive functions, such as working memory and attention, have been related to thalamo-cortical interactions, providing a novel perspective for the understanding of the neural substrate of cognition. The current review will support this perspective by summarizing evidence on the crucial role of neural oscillations in facilitating thalamo-cortical (TC) interactions during normal brain functioning and their potential impairment in ScZ. Specifically, we will focus on the relationship between NMDA-R mediated (glutamatergic) neurotransmission in TC-interactions. To this end, we will first review the functional anatomy and neurotransmitters in thalamic circuits, followed by a review of the oscillatory signatures and cognitive processes supported by TC-circuits. In the second part of the paper, data from preclinical research as well as human studies will be summarized that have implicated TC-interactions as a crucial target for NMDA-receptor hypofunctioning. Finally, we will compare these neural signatures with current evidence from ScZ-research, suggesting a potential overlap between alterations in TC-circuits as the result of NMDA-R deficits and stage-specific alterations in large-scale networks in ScZ.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

Altered brain connectivity in patients with schizophrenia is consistent across cognitive contexts

BACKGROUND

Schizophrenia has been defined as a dysconnection syndrome characterized by aberrant functional brain connectivity. Using task-based fMRI, we assessed to what extent the nature of the cognitive context may further modulate abnormal functional brain connectivity.

METHODS

We analyzed data matched for motion in patients with schizophrenia and healthy controls who performed 3 different tasks. Tasks 1 and 2 both involved emotional processing and only slighlty differed (incidental encoding v. memory recognition), whereas task 3 was a much different mental rotation task. We conducted a connectome-wide general linear model analysis aimed at identifying context-dependent and independent functional brain connectivity alterations in patients with schizophrenia.

RESULTS

After matching for motion, we included 30 patients with schizophrenia and 30 healthy controls in our study. Abnormal connectivity in patients with schizophrenia followed similar patterns regardless of the degree of similarity between cognitive tasks. Decreased connectivity was most notable in the medial prefrontal cortex, the anterior and posterior cingulate, the temporal lobe, the lobule IX of the cerebellum and the premotor cortex.

LIMITATIONS

A more circumscribed yet significant context-dependent effect might be detected with larger sample sizes or cognitive domains other than emotional and visuomotor processing.

CONCLUSION

The context-independence of functional brain dysconnectivity in patients with schizophrenia provides a good justification for pooling data from multiple experiments in order to identify connectivity biomarkers of this mental illness.

State-dependent modulation of functional connectivity in early blind individuals

Resting-state functional connectivity (RSFC) studies have provided strong evidences that visual deprivation influences the brain's functional architecture. In particular, reduced RSFC coupling between occipital (visual) and temporal (auditory) regions has been reliably observed in early blind individuals (EB) at rest. In contrast, task-dependent activation studies have repeatedly demonstrated enhanced co-activation and connectivity of occipital and temporal regions during auditory processing in EB. To investigate this apparent discrepancy, the functional coupling between temporal and occipital networks at rest was directly compared to that of an auditory task in both EB and sighted controls (SC). Functional brain clusters shared across groups and cognitive states (rest and auditory task) were defined. In EBs, we observed higher occipito-temporal correlations in activity during the task than at rest. The reverse pattern was observed in SC. We also observed higher temporal variability of occipito-temporal RSFC in EB suggesting that occipital regions in this population may play the role of a multiple demand system. Our study reveals how the connectivity profile of sighted and early blind people is differentially influenced by their cognitive state, bridging the gap between previous task-dependent and RSFC studies. Our results also highlight how inferring group-differences in functional brain architecture solely based on resting-state acquisition has to be considered with caution.

Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of "synchronicity" of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone.

Thalamic Functional Connectivity in Mild Traumatic Brain Injury: Longitudinal Associations With Patient-Reported Outcomes and Neuropsychological Tests

OBJECTIVES

(1) To examine differences in patient-reported outcomes, neuropsychological tests, and thalamic functional connectivity (FC) between patients with mild traumatic brain injury (mTBI) and individuals without mTBI and (2) to determine longitudinal associations between changes in these measures.

DESIGN

Prospective observational case-control study.

SETTING

Academic medical center.

PARTICIPANTS

A sample (N=24) of 13 patients with mTBI (mean age, 39.3±14.0y; 4 women [31%]) and 11 age- and sex-matched controls without mTBI (mean age, 37.6±13.3y; 4 women [36%]).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Resting state FC (3T magnetic resonance imaging scanner) was examined between the thalamus and the default mode network, dorsal attention network, and frontoparietal control network. Patient-reported outcomes included pain (Brief Pain Inventory), depressive symptoms (Patient Health Questionnaire-9), posttraumatic stress disorder ([PTSD] Checklist - Civilian Version), and postconcussive symptoms (Rivermead Post-Concussion Symptoms Questionnaire). Neuropsychological tests included the Delis-Kaplan Executive Function System Tower test, Trails B, and Hotel Task. Assessments occurred at 6 weeks and 4 months after hospitalization in patients with mTBI and at a single visit for controls.

RESULTS

Student t tests found increased pain, depressive symptoms, PTSD symptoms, and postconcussive symptoms; decreased performance on Trails B; increased FC between the thalamus and the default mode network; and decreased FC between the thalamus and the dorsal attention network and between the thalamus and the frontoparietal control network in patients with mTBI as compared with controls. The Spearman correlation coefficient indicated that increased FC between the thalamus and the dorsal attention network from baseline to 4 months was associated with decreased pain and postconcussive symptoms (corrected P<.05).

CONCLUSIONS

Findings suggest that alterations in thalamic FC occur after mTBI, and improvements in pain and postconcussive symptoms are correlated with normalization of thalamic FC over time.

Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness

It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects.

SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation--translating across data sources--so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information is being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.

Cognitive Effort and Schizophrenia Modulate Large-Scale Functional Brain Connectivity

Schizophrenia (SZ) is characterized by cognitive dysfunction and disorganized thought, in addition to hallucinations and delusions, and is regarded a disorder of brain connectivity. Recent efforts have been made to characterize the underlying brain network organization and interactions. However, to which degree connectivity alterations in SZ vary across different levels of cognitive effort is unknown. Utilizing independent component analysis (ICA) and methods for delineating functional connectivity measures from functional magnetic resonance imaging (fMRI) data, we investigated the effects of cognitive effort, SZ and their interactions on between-network functional connectivity during 2 levels of cognitive load in a large and well-characterized sample of SZ patients (n = 99) and healthy individuals (n = 143). Cognitive load influenced a majority of the functional connections, including but not limited to fronto-parietal and default-mode networks, reflecting both decreases and increases in between-network synchronization. Reduced connectivity in SZ was identified in 2 large-scale functional connections across load conditions, with a particular involvement of an insular network. The results document an important role of interactions between insular, default-mode, and visual networks in SZ pathophysiology. The interplay between brain networks was robustly modulated by cognitive effort, but the reduced functional connectivity in SZ, primarily related to an insular network, was independent of cognitive load, indicating a relatively general brain network-level dysfunction.

Connectivity-based whole brain dual parcellation by group ICA reveals tract structures and decreased connectivity in schizophrenia

Mapping brain connectivity based on neuroimaging data is a promising new tool for understanding brain structure and function. In this methods paper, we demonstrate that group independent component analysis (GICA) can be used to perform a dual parcellation of the brain based on its connectivity matrix (cmICA). This dual parcellation consists of a set of spatially independent source maps, and a corresponding set of paired dual maps that define the connectivity of each source map to the brain. These dual maps are called the connectivity profiles of the source maps. Traditional analysis of connectivity matrices has been used previously for brain parcellation, but the present method provides additional information on the connectivity of these segmented regions. In this paper, the whole brain structural connectivity matrices were calculated on a 5 mm(3) voxel scale from diffusion imaging data based on the probabilistic tractography method. The effect of the choice of the number of components (30 and 100) and their stability were examined. This method generated a set of spatially independent components that are consistent with the canonical brain tracts provided by previous anatomic descriptions, with the high order model yielding finer segmentations. The corpus-callosum example shows how this method leads to a robust parcellation of a brain structure based on its connectivity properties. We applied cmICA to study structural connectivity differences between a group of schizophrenia subjects and healthy controls. The connectivity profiles at both model orders showed similar regions with reduced connectivity in schizophrenia patients. These regions included forceps major, right inferior fronto-occipital fasciculus, uncinate fasciculus, thalamic radiation, and corticospinal tract. This paper provides a novel unsupervised data-driven framework that summarizes the information in a large global connectivity matrix and tests for brain connectivity differences. It has the potential for capturing important brain changes related to disease in connectivity-based disorders.

Differential functional brain network connectivity during visceral interoception as revealed by independent component analysis of fMRI TIME-series

Influential theories of brain-viscera interactions propose a central role for interoception in basic motivational and affective feeling states. Recent neuroimaging studies have underlined the insula, anterior cingulate, and ventral prefrontal cortices as the neural correlates of interoception. However, the relationships between these distributed brain regions remain unclear. In this study, we used spatial independent component analysis (ICA) and functional network connectivity (FNC) approaches to investigate time course correlations across the brain regions during visceral interoception. Functional magnetic resonance imaging (fMRI) was performed in thirteen healthy females who underwent viscerosensory stimulation of bladder as a representative internal organ at different prefill levels, i.e., no prefill, low prefill (100 ml saline), and high prefill (individually adapted to the sensations of persistent strong desire to void), and with different infusion temperatures, i.e., body warm (∼37°C) or ice cold (4-8°C) saline solution. During Increased distention pressure on the viscera, the insula, striatum, anterior cingulate, ventromedial prefrontal cortex, amygdalo-hippocampus, thalamus, brainstem, and cerebellar components showed increased activation. A second group of components encompassing the insula and anterior cingulate, dorsolateral prefrontal and posterior parietal cortices and temporal-parietal junction showed increased activity with innocuous temperature stimulation of bladder mucosa. Significant differences in the FNC were found between the insula and amygdalo-hippocampus, the insula and ventromedial prefrontal cortex, and the ventromedial prefrontal cortex and temporal-parietal junction as the distention pressure on the viscera increased. These results provide new insight into the supraspinal processing of visceral interoception originating from an internal organ.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.

Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks

The ability to attribute mental states to others, or "mentalizing," is posited to involve specific subnetworks within the overall default mode network (DMN), but this question needs clarification. To determine which default mode (DM) subnetworks are engaged by mentalizing processes, we assessed task-related recruitment of DM subnetworks. Spatial independent component analysis (sICA) applied to fMRI data using relatively high-order model (75 components). Healthy participants (n = 53, ages 17-60) performed two fMRI tasks: an interactive game involving mentalizing (Domino), a semantic memory task (SORT), and a resting state fMRI scan. sICA of the two tasks split the DMN into 10 subnetworks located in three core regions: medial prefrontal cortex (mPFC; five subnetworks), posterior cingulate/precuneus (PCC/PrC; three subnetworks), and bilateral temporoparietal junction (TPJ). Mentalizing events increased recruitment in five of 10 DM subnetworks, located in all three core DMN regions. In addition, three of these five DM subnetworks, one dmPFC subnetwork, one PCC/PrC subnetwork, and the right TPJ subnetwork, showed reduced recruitment by semantic memory task events. The opposing modulation by the two tasks suggests that these three DM subnetworks are specifically engaged in mentalizing. Our findings, therefore, suggest the unique involvement of mentalizing processes in only three of 10 DM subnetworks, and support the importance of the dmPFC, PCC/PrC, and right TPJ in mentalizing as described in prior studies.

Enhanced disease characterization through multi network functional normalization in fMRI

Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well-studied. Also, these networks exhibit temporal and spatial modulation during cognitive task vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar et al., 2011) has shown that structural and functional data may not have direct one-to-one correspondence and functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task. The results of this study and the existence of the neural activity patterns in multiple networks motivates us to investigate multiple resting-state networks as a single fusion template for functional normalization for multi groups of subjects. We extend the previous approach (Khullar et al., 2011) by co-registering multi group of subjects (healthy control and schizophrenia patients) and by utilizing multiple resting-state networks (instead of just one) as a single fusion template for functional normalization. In this paper we describe the initial steps toward using multiple resting-state networks as a single fusion template for functional normalization. A simple wavelet-based image fusion approach is presented in order to evaluate the feasibility of combining multiple functional networks. Our results showed improvements in both the significance of group statistics (healthy control and schizophrenia patients) and the spatial extent of activation when a multiple resting-state network applied as a single fusion template for functional normalization after the conventional structural normalization. Also, our results provided evidence that the improvement in significance of group statistics lead to better accuracy results for classification of healthy controls and schizophrenia patients.

Disrupted thalamic resting-state functional networks in schizophrenia

The thalamus plays a key role in filtering or gating information and has extensive interconnectivity with other brain regions. Recent studies provide evidence of thalamus abnormality in schizophrenia, but the resting functional networks of the thalamus in schizophrenia is still unclear. We characterize the thalamic resting-state networks (RSNs) in 72 patients with schizophrenia and 73 healthy controls, using a standard seed-based whole-brain correlation. In comparison with controls, patients exhibited enhance thalamic connectivity with bilateral precentral gyrus, dorsal medial frontal gyrus, middle occipital gyrus, and lingual gyrus. Reduced thalamic connectivity in schizophrenia was found in bilateral superior frontal gyrus, anterior cingualte cortex, inferior parietal lobe, and cerebellum. Our findings question the “disconnectivity model” of schizophrenia by showing the over-connected thalamic network during resting state in schizophrenia and highlight the thalamus as a key hub in the schizophrenic network abnormality.

Multimodal based classification of schizophrenia patients

Schizophrenia is currently diagnosed by physicians through clinical assessment and their evaluation of patient's self-reported experiences over the longitudinal course of the illness. There is great interest in identifying biologically based markers at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity shows promise in providing individual subject predictive power. The majority of previous studies considered the analysis of functional connectivity during resting-state using only fMRI. However, exclusive reliance on fMRI to generate such networks, may limit inference on dysfunctional connectivity, which is hypothesized to underlie patient symptoms. In this work, we propose a framework for classification of schizophrenia patients and healthy control subjects based on using both fMRI and band limited envelope correlation metrics in MEG to interrogate functional network components in the resting state. Our results show that the combination of these two methods provide valuable information that captures fundamental characteristics of brain network connectivity in schizophrenia. Such information is useful for prediction of schizophrenia patients. Classification accuracy performance was improved significantly (up to ≈ 7%) relative to only the fMRI method and (up to ≈ 21%) relative to only the MEG method.