Aim

The hippocampus is considered a key region in schizophrenia pathophysiology, but the nature of hippocampal subregion abnormalities and how they contribute to disease expression remain to be fully determined. This study reviews findings from schizophrenia hippocampal subregion volumetric and physiological imaging studies published within the last decade.

Methods

The PubMed database was searched for publications on hippocampal subregion volume and physiology abnormalities in schizophrenia and their findings were reviewed.

Results

The main replicated findings include smaller CA1 volumes and CA1 hyperactivation in schizophrenia, which may be predictive of conversion in individuals at clinical high risk of psychosis, smaller CA1 and CA4/DG volumes in first-episode schizophrenia, and more widespread smaller hippocampal subregion volumes with longer duration of illness. Several studies have reported relationships between hippocampal subregion volumes and declarative memory or symptom severity.

Conclusions

Together these studies provide support for hippocampal formation circuitry models of schizophrenia. These initial findings must be taken with caution as the scientific community is actively working on hippocampal subregion method improvement and validation. Further improvements in our understanding of the nature of hippocampal formation subregion involvement in schizophrenia will require the collection of structural and physiological imaging data at submillimeter voxel resolution, standardization and agreement of atlases, adequate control for possible confounding factors, and multi-method validation of findings. Despite the need for cautionary interpretation of the initial findings, we believe that improved localization of hippocampal subregion abnormalities in schizophrenia holds promise for the identification of disease contributing mechanisms.

Hippocampal formation (HF) volume and episodic memory performance are substantially heritable, but HF subregion heritability estimates and their possible shared genetic variance with episodic memory performance remain to be determined. This study provides heritability estimates for hippocampal subregions (e.g, Cornu Amonis, Subiculum, Parasubiculum, Molecular and Granule Cell Layers of the Dentate Gryus) and Total HF volumes obtained using FreeSurfer 6.0. In addition, this study assesses the heritability of object sequence and verbal episodic memory performance, and the amount of shared genetic variance between HF subregions and Total HF volume and episodic memory performance. HF volumes were obtained from high-resolution brain scans from a sample of 499 siblings (mean age±SD=30.0±3.1, 203 men), including 51 monozygotic and 46 dizygotic twin pairs and 305 non-twin siblings, collected by the Human Connectome Project (www.humanconnectome.org). Heritability estimates for HF subregions ranged from 0.42-0.87 and shared genetic variance of HF subregions with hippocampal volume was substantial (mean=0.79, range=0.50-0.98). HF subregion volumes residualized for Total HF and percent HF subregion volumes were also found to be substantially heritable (range=0.04-0.86 and 0.07-0.84, respectively). Verbal (h2=0.47) but not object sequence episodic memory was found to be significantly heritable; though the amount of shared genetic variance between HF subregions and verbal episodic memory was low (mean=0.10, range=0.01-0.20). These findings suggest that HF subregion volumes are heritable and can be used as quantitative phenotypes in genetic association studies. The low shared genetic variance between HF subregions and verbal episodic memory suggests that quantitative trait analyses may not benefit from including both HF volume and episodic memory as bivariate traits in healthy individuals. The extent to which HF subregion volumes share genetic variance with neuropsychiatric disorders, and as such add value to our ability to identify genetic risk loci for these disorders, remains to be determined.

Studies on structural brain abnormalities in individuals with autism spectrum disorders (ASD) have been of limited size and many findings have not been replicated. In the largest ASD brain morphology study to date, we compared subcortical, total brain (TBV), and intracranial (ICV) volumes between 472 subjects with DSM-IV ASD diagnoses and 538 healthy volunteers (age range: 6-64 years), obtained from high-resolution structural brain scans provided by the Autism Brain Imaging Data Exchange (ABIDE). Compared to healthy volunteers, we found significantly larger pallidum (Cohen's d=0.15) and lateral ventricle volumes (Cohen's d=0.18) in ASD. These enlargements were independent of total brain volume and IQ, passed FDR correction for multiple comparisons, and were observed in overall, male-only, and medication-free subjects. In addition, intracranial, hippocampal, and caudate volumes were enlarged in ASD at a nominal statistical threshold of p<0.05. This study provides the first robust evidence for pallidum enlargement in ASD independent from TBV and encourages further study of the functional role of the pallidum in individuals with autism spectrum disorder.

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

Revealing associations among various structural and functional patterns of the brain can yield highly informative results about the healthy and disordered brain. Studies using neuroimaging data have more recently begun to utilize the information within as well as across various functional and anatomical domains (i.e., groups of brain networks). However, most whole-brain approaches assume similar complexity of interactions throughout the brain. Here we investigate the hypothesis that interactions between brain networks capture varying amounts of complexity, and that we can better capture this information by varying the complexity of the model subspace structure based on available training data. To do this, we employ a Bayesian optimization-based framework known as the Tree Parzen Estimator (TPE) to identify, exploit and analyze patterns of variation in the information encoded by temporal information extracted from functional magnetic resonance imaging (fMRI) subdomains of the brain. Using a repeated cross-validation procedure on a schizophrenia classification task, we demonstrate evidence that interactions between specific functional subdomains are better characterized by more sophisticated model architectures compared to less complicated ones required by the others for optimally contributing towards classification and understanding the brain's functional interactions. We show that functional subdomains known to be involved in schizophrenia require more complex architectures to optimally unravel discriminatory information about the disorder. Our study points to the need for adaptive, hierarchical learning frameworks that cater differently to the features from different subdomains, not only for a better prediction but also for enabling the identification of features predicting the outcome of interest.

Accumulating evidence points to the association of epilepsy, particularly, temporal lobe epilepsy (TLE), with psychiatric disorders, such as schizophrenia. Among these illnesses, the hippocampus is considered the regional focal point of the brain, playing an important role in cognition, psychosis, and seizure activity and potentially suggesting common etiologies and pathophysiology of TLE and schizophrenia. In the present review, we overview abnormal network connectivity between the dentate gyrus (DG) and the Cornus Ammonis area 3 (CA3) subregions of the hippocampus relative to the induction of epilepsy and schizophrenia. In light of our recent finding on the misguidance of hippocampal mossy fiber projection in the rodent model of schizophrenia, we discuss whether ectopic mossy fiber projection is a commonality in order to evoke TLE as well as symptoms related to schizophrenia.

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.

Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.

Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Results: Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Conclusions: Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run k-means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the k-means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.