Two multimodal neuroimaging subtypes of obsessive-compulsive disorder disclosed by semi-supervised machine learning

BACKGROUND

Obsessive-compulsive disorder (OCD) is a highly heterogeneous mental condition with a diverse symptom. Existing studies classified OCD on the basis of conventional phenomenology-based taxonomy ignoring the fact that the same subtype identified in accordance with clinical symptom may have different mechanisms and treatment responses.

METHODS

This research involved 50 medicine-free patients with OCD and 50 matched healthy controls (HCs). All the participants were subjected to structural and functional magnetic resonance imaging (MRI). Voxel-based morphometry (VBM) and amplitude of low frequency fluctuation (ALFF) were used to evaluate gray matter volume (GMV) and spontaneous neuronal activities at rest respectively. Similarity network fusion (SNF) was utilized to integrate GMVs and spontaneous neuronal activities, and heterogeneity by discriminant analysis was applied to characterise OCD subtypes.

RESULTS

Two OCD subtypes were identified: Subtype 1 exhibited decreased GMVs (i.e., left inferior temporal gyrus, right supplementary motor area and right lingual gyrus) and increased ALFF value (i.e., right orbitofrontal cortex), whereas subtype 2 exhibited increased GMVs (i.e., left cuneus, right precentral gyrus, left postcentral gyrus and left hippocampus) and decreased ALFF value (i.e., right caudate nucleus). Furthermore, the altered GMVs was negatively correlated with abnormal ALFF values in both subtype 1 and 2.

LIMITATIONS

This study requires further validation via a larger, independent dataset and should consider the potential influences of psychotropic medication on OCD patients' brain activities.

CONCLUSIONS

Results revealed two reproducible subtypes of OCD based on underlying multimodal neuroimaging and provided new perspectives on the classification of OCD.

Age-related differences in fMRI subsequent memory effects are directly linked to local grey matter volume differences

Episodic memory performance declines with increasing age, and older adults typically show reduced activation of inferior temporo-parietal cortices in functional magnetic resonance imaging (fMRI) studies of episodic memory formation. Given the age-related cortical volume loss, it is conceivable that age-related reduction of memory-related fMRI activity may be partially attributable to reduced grey matter volume (GMV). We performed a voxel-wise multimodal neuroimaging analysis of fMRI correlates of successful memory encoding, using regional GMV as covariate. In a large cohort of healthy adults (106 young, 111 older), older adults showed reduced GMV across the entire neocortex and reduced encoding-related activation of inferior temporal and parieto-occipital cortices compared to young adults. Importantly, these reduced fMRI activations during successful encoding could in part be attributed to lower regional GMV. Our results highlight the importance of controlling for structural MRI differences in fMRI studies in older adults but also demonstrate that age-related differences in memory-related fMRI activity cannot be attributed to structural variability alone.

Investigation of electro-vascular phase-amplitude coupling during an auditory task

Multimodal neuroimaging using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provides complementary views of cortical processes, including those related to auditory processing. However, current multimodal approaches often overlook potential insights that can be gained from nonlinear interactions between electrical and hemodynamic signals. Here, we explore electro-vascular phase-amplitude coupling (PAC) between low-frequency hemodynamic and high-frequency electrical oscillations during an auditory task. We further apply a temporally embedded canonical correlation analysis (tCCA)-general linear model (GLM)-based correction approach to reduce the possible effect of systemic physiology on fNIRS recordings. Before correction, we observed significant PAC between fNIRS and broadband EEG in the frontal region (p ≪ 0.05), β (p ≪ 0.05) and γ (p = 0.010) in the left temporal/temporoparietal (left auditory; LA) region, and γ (p = 0.032) in the right temporal/temporoparietal (right auditory; RA) region across the entire dataset. Significant differences in PAC across conditions (task versus silence) were observed in LA (p = 0.023) and RA (p = 0.049) γ sub-bands and in lower frequency (5-20 Hz) frontal activity (p = 0.005). After correction, significant fNIRS-γ-band PAC was observed in the frontal (p = 0.021) and LA (p = 0.025) regions, while fNIRS-α (p = 0.003) and fNIRS-β (p = 0.041) PAC were observed in RA. Decreased frontal γ-band (p = 0.008) and increased β-band (p ≪ 0.05) PAC were observed during the task. These outcomes represent the first characterization of electro-vascular PAC between fNIRS and EEG signals during an auditory task, providing insights into electro-vascular coupling in auditory processing.

Functional and structural connectivity in the Papez circuit in different stages of Alzheimer's disease

OBJECTIVE

Alzheimer's disease (AD) is a progressive neurodegenerative continuum with memory impairment. We aimed to examine the detailed functional (FC) and structural connectivity (SC) pattern of the Papez circuit, known as the memory circuit, along the AD.

METHODS

MRI data of 15 patients diagnosed with AD dementia (ADD), 15 patients with the amnestic mild cognitive impairment (MCI), and 15 patients with subjective cognitive impairment were analyzed. The FC analyses were performed between main nodes of the Papez circuit, and the SC was quantified as fractional anisotropy (FA) of the main white matter pathways of the Papez circuit.

RESULTS

The FC between the retrosplenial (RSC) and parahippocampal cortices (PHC) was the earliest affected FC, while a manifest SC change in the ventral cingulum and fornix was observed in the later ADD stage. The RSC-PHC FC and the ventral cingulum FA efficiently predicted the memory performance of the non-demented participants.

CONCLUSIONS

Our findings revealed the importance of the Papez circuit as target regions along the AD.

SIGNIFICANCE

The ventral cingulum connecting the RSC and PHC, a critical overlap area between the Papez circuit and the default mode network, seems to be a target region associated with the earliest objective memory findings in AD.

A 16-channel loop array for in vivo macaque whole-brain imaging at 7 T

Combining multimodal approaches with functional magnetic resonance imaging (fMRI) has catapulted the research on brain circuitries of non-human primates (NHPs) into a new era. However, many studies are constrained by a lack of appropriate RF coils. In this study,a single loop transmit and 16-channel receive array coil was constructed for brain imaging of macaques at 7 Tesla (7 T). The 16 receive channels were mounted on a 3D-printed helmet-shaped form closely fiting the macaque head, with fourteen openings arranged for multimodal devices around the cortical regions. Coil performance was evaluated by quantifying and comparing signal-to-noise ratio (SNR) maps, noise correlations, g-factor maps and flip-angle maps with a 28-channel commercial knee coil. The in vivo results suggested that the macaque coil has higher SNR in cortical regions and better acceleration ability in parallel imaging, which may benefit revealing mesoscale organizations in the brain.

Neuroimaging Investigations of Obesity: a Review of the Treatment of Sex from 2010

PURPOSE OF REVIEW

To summarize the results of adult obesity neuroimaging studies (structural, resting-state, task-based, diffusion tensor imaging) published from 2010, with a focus on the treatment of sex as an important biological variable in the analysis, and identify gaps in sex difference research.

RECENT FINDINGS

Neuroimaging studies have shown obesity-related changes in brain structure, function, and connectivity. However, relevant factors such as sex are often not considered. We conducted a systematic review and keyword co-occurrence analysis. Literature searches identified 6281 articles, of which 199 met inclusion criteria. Among these, only 26 (13%) considered sex as an important variable in the analysis, directly comparing the sexes (n = 10; 5%) or providing single-sex/disaggregated data (n = 16, 8%); the remaining studies controlled for sex (n = 120, 60%) or did not consider sex in the analysis (n = 53, 27%). Synthesizing sex-based results, obesity-related parameters (e.g., body mass index, waist circumference, obese status) may be generally associated with more robust morphological alterations in men and more robust structural connectivity alterations in women. Additionally, women with obesity generally expressed increased reactivity in affect-related regions, while men with obesity generally expressed increased reactivity in motor-related regions; this was especially true under a fed state. The keyword co-occurrence analysis indicated that sex difference research was especially lacking in intervention studies. Thus, although sex differences in the brain associated with obesity are known to exist, a large proportion of the literature informing the research and treatment strategies of today has not specifically examined sex effects, which is needed to optimize treatment.

A revisit of the amygdala theory of autism: Twenty years after

The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.

Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review

Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.

Effects of a probiotic add-on treatment on fronto-limbic brain structure, function, and perfusion in depression: Secondary neuroimaging findings of a randomized controlled trial

BACKGROUND

Probiotics are suggested to improve depressive symptoms via the microbiota-gut-brain axis. We have recently shown a beneficial clinical effect of probiotic supplementation in patients with depression. Their underlying neural mechanisms remain unknown.

METHODS

A multimodal neuroimaging approach including diffusion tensor imaging, resting-state functional MRI, and arterial spin labeling was used to investigate the effects of a four-weeks probiotic supplementation on fronto-limbic brain structure, function, and perfusion and whether these effects were related to symptom changes.

RESULTS

Thirty-two patients completed both imaging assessments (18 placebo and 14 probiotics group). Probiotics maintained mean diffusivity in the left uncinate fasciculus, stabilized it in the right uncinate fasciculus, and altered resting-state functional connectivity (rsFC) between limbic structures and the temporal pole to a cluster in the precuneus. Moreover, a cluster in the left superior parietal lobule showed altered rsFC to the subcallosal cortex, the left orbitofrontal cortex, and limbic structures after probiotics. In the probiotics group, structural and functional changes were partly related to decreases in depressive symptoms.

LIMITATIONS

This study has a rather small sample size. An additional follow-up MRI session would be interesting for seeing clearer changes in the relevant brain regions as clinical effects were strongest in the follow-up.

CONCLUSION

Probiotic supplementation is suggested to prevent neuronal degeneration along the uncinate fasciculus and alter fronto-limbic rsFC, effects that are partly related to the improvement of depressive symptoms. Elucidating the neural mechanisms underlying probiotics' clinical effects on depression provide potential targets for the development of more precise probiotic treatments.

An orderly sequence of autonomic and neural events at transient arousal changes

Resting-state functional magnetic resonance imaging (rsfMRI) allows the study of functional brain connectivity based on spatially structured variations in neuronal activity. Proper evaluation of connectivity requires removal of non-neural contributions to the fMRI signal, in particular hemodynamic changes associated with autonomic variability. Regression analysis based on autonomic indicator signals has been used for this purpose, but may be inadequate if neuronal and autonomic activities covary. To investigate this potential co-variation, we performed rsfMRI experiments while concurrently acquiring electroencephalography (EEG) and autonomic indicator signals, including heart rate, respiratory depth, and peripheral vascular tone. We identified a recurrent and systematic spatiotemporal pattern of fMRI (named as fMRI cascade), which features brief signal reductions in salience and default-mode networks and the thalamus, followed by a biphasic global change with a sensory-motor dominance. This fMRI cascade, which was mostly observed during eyes-closed condition, was accompanied by large EEG and autonomic changes indicative of arousal modulations. Importantly, the removal of the fMRI cascade dynamics from rsfMRI diminished its correlations with various signals. These results suggest that the rsfMRI correlations with various physiological and neural signals are not independent but arise, at least partly, from the fMRI cascades and associated neural and physiological changes at arousal modulations.

The role of glutamate and GABA in cognitive dysfunction in schizophrenia and mood disorders - A systematic review of magnetic resonance spectroscopy studies

Epidemiologic, genetic, and neurobiological studies suggest considerable overlap between schizophrenia and mood disorders. Importantly, both disorders are associated with a broad range of cognitive deficits as well as altered glutamatergic and GABAergic neurometabolism. We conducted a systematic review of magnetic resonance spectroscopy (MRS) studies investigating the relationship between glutamatergic and GABAergic neurometabolites and cognition in schizophrenia spectrum disorders and mood disorders. A literature search in Pubmed of studies published before April 15, 2019 was conducted and 37 studies were deemed eligible for systematic review. We found that alterations in glutamatergic and GABAergic neurotransmission have been identified relatively consistently in both schizophrenia and mood disorders. However, because of the vast heterogeneity of published studies in terms of illness stage, medication exposure, MRS acquisition parameters and data post-processing strategies, we still do not understand the relationship between those neurotransmitters and cognitive dysfunction in mental illness, which is a critical initial step for rational drug development. Our findings emphasize the need for coordinated multi-center studies that characterize cognitive function and its biological substrates in large and well-defined clinical populations, using harmonized imaging sequences and analytical methods with the goal to elucidate the underlying pathophysiological mechanisms and to inform future clinical trials.

Multi-feature computational framework for combined signatures of dementia in underrepresented settings

OBJECTIVE

The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings.

APPROACH

We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, MRI, and EEG/fMRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat).

MAIN RESULTS

The multimodal model yielded high AUC classification values (bvFTD patients vs. HCs: 0.93 (±0.01); AD patients vs. HCs: 0.95 (±0.01); bvFTD vs. AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). Results proved robust against multimodal heterogeneity, sociodemographic variability, and missing data.

SIGNIFICANCE

The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.

Functional network and structural connections involved in picture naming

We mapped the left hemisphere cortical regions and fiber bundles involved in picture naming in adults by integrating task-based fMRI with dMRI tractography. We showed that a ventral pathway that "maps image and sound to meaning" involves the middle occipital, inferior temporal, superior temporal, inferior frontal gyri, and the temporal pole where a signal exchange is made possible by the inferior fronto-occipital, inferior longitudinal, middle longitudinal, uncinate fasciculi, and the extreme capsule. A dorsal pathway that "maps sound to speech" implicates the inferior temporal, superior temporal, inferior frontal, precentral gyri, and the supplementary motor area where the arcuate fasciculus and the frontal aslant ensure intercommunication. This study provides a neurocognitive model of picture naming and supports the hypothesis that the ventral indirect route passes through the temporal pole. This further supports the idea that the inferior and superior temporal gyri may play pivotal roles within the dual-stream framework of language.

Generalized Liquid Association Analysis for Multimodal Data Integration

Multimodal data are now prevailing in scientific research. One of the central questions in multimodal integrative analysis is to understand how two data modalities associate and interact with each other given another modality or demographic variables. The problem can be formulated as studying the associations among three sets of random variables, a question that has received relatively less attention in the literature. In this article, we propose a novel generalized liquid association analysis method, which offers a new and unique angle to this important class of problems of studying three-way associations. We extend the notion of liquid association of Li (2002) from the univariate setting to the sparse, multivariate, and high-dimensional setting. We establish a population dimension reduction model, transform the problem to sparse Tucker decomposition of a three-way tensor, and develop a higher-order orthogonal iteration algorithm for parameter estimation. We derive the non-asymptotic error bound and asymptotic consistency of the proposed estimator, while allowing the variable dimensions to be larger than and diverge with the sample size. We demonstrate the efficacy of the method through both simulations and a multimodal neuroimaging application for Alzheimer's disease research.

Neuronal efficiency following n-back training task is accompanied by a higher cerebral glucose metabolism

Recent functional magnetic resonance imaging (fMRI) studies revealed lower neural activation during processing of an n-back task following working memory training, indicating a training-related increase in neural efficiency. In the present study, we asked if the training induced regional neural activation is accompanied by changes in glucose consumption. An active control and an experimental group of healthy middle-aged volunteers conducted 32 sessions of visual and verbal n-back trainings over 8 weeks. We analyzed data of 52 subjects (25 experimental and 27 control group) for practice effects underlying verbal working memory task and 50 subjects (24 experimental and 26 control group) for practice effects underlying visual WM task. The samples of these two tasks were nearly identical (data of 47 subjects were available for both verbal and visual tasks). Both groups completed neuroimaging sessions at a hybrid PET/MR system before and after training. Each session included criterion task fMRI and resting state positron emission tomography with FDG (FDG-PET). As reported previously, lower neural activation following n-back training was found in regions of the fronto-parieto-cerebellar circuitry during a verbal n-back task. Notably, these changes co-occurred spatially with a higher relative FDG-uptake. Decreased neural activation within regions of the fronto-parietal network during visual n-back task did not show co-occurring changes in relative FDG-uptake. There was no direct association between neuroimaging and behavioral measures, which could be due to the inter-subjects' variability in reaching capacity limits. Our findings provide new details for working memory training induced neural efficiency on a molecular level by integrating FDG-PET and fMRI measures.

Four-modality imaging of unmedicated subjects with schizophrenia: 18F-fluorodeoxyglucose and 18F-fallypride PET, diffusion tensor imaging, and MRI

Diminished prefrontal function, dopaminergic abnormalities in the striatum and thalamus, reductions in white matter integrity and frontotemporal gray matter deficits are the most replicated findings in schizophrenia. We used four imaging modalities (¹⁸F-fluorodeoxyglucose and ¹⁸F-fallypride PET, diffusion tensor imaging, structural MRI) in 19 healthy and 25 schizophrenia subjects to assess the relationship between functional (dopamine D₂/D₃ receptor binding potential, glucose metabolic rate) and structural (fractional anisotropy, MRI) correlates of schizophrenia and their additive diagnostic prediction potential. Multivariate ANOVA was used to compare structural and functional image sets for identification of schizophrenia. Integration of data from all four modalities yielded better predictive power than less inclusive combinations, specifically in the thalamus, left dorsolateral prefrontal and temporal regions. Among the modalities, fractional anisotropy showed highest discrimination in white matter whereas ¹⁸F-fallypride binding showed highest discrimination in gray matter. Structural and functional modalities displayed comparable discriminative power but different topography, with higher sensitivity of structural modalities in the left prefrontal region. Combination of functional and structural imaging modalities with inclusion of both gray and white matter appears most effective in diagnostic discrimination. The highest sensitivity of ¹⁸F-fallypride PET to gray matter changes in schizophrenia supports the primacy of dopaminergic abnormalities in its pathophysiology.

A methodological scoping review of the integration of fMRI to guide dMRI tractography. What has been done and what can be improved: A 20-year perspective

Combining MRI modalities is a growing trend in neurosciences. It provides opportunities to investigate the brain architecture supporting cognitive functions. Integrating fMRI activation to guide dMRI tractography offers potential advantages over standard tractography methods. A quick glimpse of the literature on this topic reveals that this technique is challenging, and no consensus or "best practices" currently exist, at least not within a single document. We present the first attempt to systematically analyze and summarize the literature of 80 studies that integrated task-based fMRI results to guide tractography, over the last two decades. We report 19 findings that cover challenges related to sample size, microstructure modelling, seeding methods, multimodal space registration, false negatives/positives, specificity/validity, gray/white matter interface and more. These findings will help the scientific community (1) understand the strengths and limitations of the approaches, (2) design studies using this integrative framework, and (3) motivate researchers to fill the gaps identified. We provide references toward best practices, in order to improve the overall result's replicability, sensitivity, specificity, and validity.

Integrative Factor Regression and Its Inference for Multimodal Data Analysis

Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis based supervised modeling of multimodal data. In this article, we consider an integrative linear regression model that is built upon the latent factors extracted from multimodal data. We address three important questions: how to infer the significance of one data modality given the other modalities in the model; how to infer the significance of a combination of variables from one modality or across different modalities; and how to quantify the contribution, measured by the goodness-of-fit, of one data modality given the others. When answering each question, we explicitly characterize both the benefit and the extra cost of factor analysis. Those questions, to our knowledge, have not yet been addressed despite wide use of factor analysis in integrative multimodal analysis, and our proposal bridges an important gap. We study the empirical performance of our methods through simulations, and further illustrate with a multimodal neuroimaging analysis.

Fifteen years of urea cycle disorders brain research: Looking back, looking forward

Urea cycle disorders (UCD) are inherited diseases resulting from deficiency in one of six enzymes or two carriers that are required to remove ammonia from the body. UCD may be associated with neurological damage encompassing a spectrum from asymptomatic/mild to severe encephalopathy, which results in most cases from Hyperammonemia (HA) and elevation of other neurotoxic intermediates of metabolism. Electroencephalography (EEG), Magnetic resonance imaging (MRI) and Proton Magnetic resonance spectroscopy (MRS) are noninvasive measures of brain function and structure that can be used during HA to guide management and provide prognostic information, in addition to being research tools to understand the pathophysiology of UCD associated brain injury. The Urea Cycle Rare disorders Consortium (UCDC) has been invested in research to understand the immediate and downstream effects of hyperammonemia (HA) on brain using electroencephalogram (EEG) and multimodal brain MRI to establish early patterns of brain injury and to track recovery and prognosis. This review highlights the evolving knowledge about the impact of UCD and HA in particular on neurological injury and recovery and use of EEG and MRI to study and evaluate prognostic factors for risk and recovery. It recognizes the work of others and discusses the UCDC's prior work and future research priorities.

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.

The early postpartum period - Differences between women with and without a history of depression

Depression is a highly recurrent disorder. When in remission, it affords an important opportunity to understand the state-independent neurobiological alterations, as well as the socio-demographic characteristics, that likely contribute to the recurrence of major depressive disorder (MDD). The present study examined 110 euthymic women in their early postpartum period. A comparison was made between participants with (n = 20) and without (n = 90) a history of MDD by means of a multimodal approach including an fMRI experiment, assessment of hair cortisol concentration (HCC) and a clinical anamnestic interview. Women with a personal history of MDD were found to have decreased resting-state functional connectivity (RSFC) between the lateral parietal cortex (LPC) and the posterior cingulate cortex (PCC), and their Edinburgh Postnatal Depression Scale (EPDS) scores were significantly higher shortly after childbirth. More often than not, these women also had a family history of MDD. While women with no history of depression showed a negative association between hair cortisol concentration (HCC) and gray matter volume (GMV) in the medial orbitofrontal cortex (mOFC), the opposite trend was seen in women with a history of depression. This implies that women with remitted depression show distinctive neural phenotypes with subclinical residual symptoms, which likely predispose them to later depressive episodes.

The NIMH Intramural Longitudinal Study of the Endocrine and Neurobiological Events Accompanying Puberty: Protocol and rationale for methods and measures

Delineating the relationship between human neurodevelopment and the maturation of the hypothalamic-pituitary-gonadal (HPG) axis during puberty is critical for investigating the increase in vulnerability to neuropsychiatric disorders that is well documented during this period. Preclinical research demonstrates a clear association between gonadal production of sex steroids and neurodevelopment; however, identifying similar associations in humans has been complicated by confounding variables (such as age) and the coactivation of two additional endocrine systems (the adrenal androgenic system and the somatotropic growth axis) and requires further elucidation. In this paper, we present the design of, and preliminary observations from, the ongoing NIMH Intramural Longitudinal Study of the Endocrine and Neurobiological Events Accompanying Puberty. The aim of this study is to directly examine how the increase in sex steroid hormone production following activation of the HPG-axis (i.e., gonadarche) impacts neurodevelopment, and, additionally, to determine how gonadal development and maturation is associated with longitudinal changes in brain structure and function in boys and girls. To disentangle the effects of sex steroids from those of age and other endocrine events on brain development, our study design includes 1) selection criteria that establish a well-characterized baseline cohort of healthy 8-year-old children prior to the onset of puberty (e.g., prior to puberty-related sex steroid hormone production); 2) temporally dense longitudinal, repeated-measures sampling of typically developing children at 8-10 month intervals over a 10-year period between the ages of eight and 18; 3) contemporaneous collection of endocrine and other measures of gonadal, adrenal, and growth axis function at each timepoint; and 4) collection of multimodal neuroimaging measures at these same timepoints, including brain structure (gray and white matter volume, cortical thickness and area, white matter integrity, myelination) and function (reward processing, emotional processing, inhibition/impulsivity, working memory, resting-state network connectivity, regional cerebral blood flow). This report of our ongoing longitudinal study 1) provides a comprehensive review of the endocrine events of puberty; 2) details our overall study design; 3) presents our selection criteria for study entry (e.g., well-characterized prepubertal baseline) along with the endocrinological considerations and guiding principles that underlie these criteria; 4) describes our longitudinal outcome measures and how they specifically relate to investigating the effects of gonadal development on brain development; and 5) documents patterns of fMRI activation and resting-state networks from an early, representative subsample of our cohort of prepubertal 8-year-old children.

Association of quality of life with structural, functional and molecular brain imaging in community-dwelling older adults

BACKGROUND

As the population ages, maintaining mental health and well-being of older adults is a public health priority. Beyond objective measures of health, self-perceived quality of life (QoL) is a good indicator of successful aging. In older adults, it has been shown that QoL is related to structural brain changes. However, QoL is a multi-faceted concept and little is known about the specific relationship of each QoL domain to brain structure, nor about the links with other aspects of brain integrity, including white matter microstructure, brain perfusion and amyloid deposition, which are particularly relevant in aging. Therefore, we aimed to better characterize the brain biomarkers associated with each QoL domain using a comprehensive multimodal neuroimaging approach in older adults.

METHODS

One hundred and thirty-five cognitively unimpaired older adults (mean age ± SD: 69.4 ± 3.8 y) underwent structural and diffusion magnetic resonance imaging, together with early and late florbetapir positron emission tomography scans. QoL was assessed using the brief version of the World Health Organization's QoL instrument, which allows measuring four distinct domains of QoL: self-perceived physical health, psychological health, social relationships and environment. Multiple regression analyses were carried out to identify the independent global neuroimaging predictor(s) of each QoL domain, and voxel-wise analyses were then conducted with the significant predictor(s) to highlight the brain regions involved. Age, sex, education and the other QoL domains were entered as covariates in these analyses. Finally, forward stepwise multiple regressions were conducted to determine the specific items of the relevant QoL domain(s) that contributed the most to these brain associations.

RESULTS

Only physical health QoL was associated with global neuroimaging values, specifically gray matter volume and white matter mean kurtosis, with higher physical health QoL being associated with greater brain integrity. These relationships were still significant after correction for objective physical health and physical activity measures. No association was found with global brain perfusion or global amyloid deposition. Voxel-wise analyses revealed that the relationships with physical health QoL concerned the anterior insula and ventrolateral prefrontal cortex, and the corpus callosum, corona radiata, inferior frontal white matter and cingulum. Self-perceived daily living activities and self-perceived pain and discomfort were the items that contributed the most to these associations with gray matter volume and white matter mean kurtosis, respectively.

CONCLUSIONS

Better self-perceived physical health, encompassing daily living activities and pain and discomfort, was the only QoL domain related to brain structural integrity including higher global gray matter volume and global white matter microstructural integrity in cognitively unimpaired older adults. The relationships involved brain structures belonging to the salience network, the pain pathway and the empathy network. While previous studies showed a link between objective measures of physical health, our findings specifically highlight the relevance of monitoring and promoting self-perceived physical health in the older population. Longitudinal studies are needed to assess the direction and causality of the relationships between QoL and brain integrity.

White-matter tract connecting anterior insula to nucleus accumbens predicts greater future motivation in adolescents

The motivation to approach or avoid incentives can change during adolescence. Advances in neuroimaging allow researchers to characterize specific brain circuits that underlie these developmental changes. Whereas activity in the nucleus accumbens (NAcc) can predict approach toward incentive gain, activity in anterior insula (AIns) is associated with avoidance of incentive loss. Recent research characterized the structural white-matter tract connecting the two brain regions, but the tract has neither been characterized in adolescence nor linked to functional activity during incentive anticipation. In this study, we collected diffusion MRI and characterized the tract connecting the AIns to the NAcc for the first time in early adolescents. We then measured NAcc functional activity during a monetary incentive delay task and found that structural coherence of the AIns-NAcc tract is correlated with decreased functional activity at the NAcc terminal of the tract during anticipation of no incentives. In adolescents who completed an assessment 2 years later, we found that AIns-NAcc tract coherence could predict greater future self-reported motivation, and that NAcc functional activity could statistically mediate this association. Together, the findings establish links from brain structure to function to future motivation and provide targets to study the reciprocal development of brain structure and function.

Neuroimaging Biomarkers and Neurocognitive Outcomes in Pediatric Medulloblastoma Patients: a Systematic Review

Medulloblastoma is a malign posterior fossa brain tumor, mostly occurring in childhood. The CNS-directed chemoradiotherapy treatment can be very harmful to the developing brain and functional outcomes of these patients. However, what the underlying neurotoxic mechanisms are remain inconclusive. Hence, this review summarizes the existing literature on the association between advanced neuroimaging and neurocognitive changes in patients that were treated for pediatric medulloblastoma. The PubMed/Medline database was extensively screened for studies investigating the link between cognitive outcomes and multimodal magnetic resonance (MR) imaging in childhood medulloblastoma survivors. A behavioral meta-analysis was performed on the available IQ scores. A total of 649 studies were screened, of which 22 studies were included. Based on this literature review, we conclude medulloblastoma patients to be at risk for white matter volume loss, more frequent white matter lesions, and changes in white matter microstructure. Such microstructural alterations were associated with lower IQ, which reached the clinical cut-off in survivors across studies. Using functional MR scans, changes in activity were observed in cerebellar areas, associated with working memory and processing speed. Finally, cerebral microbleeds were encountered more often, but these were not associated with cognitive outcomes. Regarding intervention studies, computerized cognitive training was associated with changes in prefrontal and cerebellar activation and physical training might result in microstructural and cortical alterations. Hence, to better define the neural targets for interventions in pediatric medulloblastoma patients, this review suggests working towards neuroimaging-based predictions of cognitive outcomes. To reach this goal, large multimodal prospective imaging studies are highly recommended.

A deep learning fusion model for brain disorder classification: Application to distinguishing schizophrenia and autism spectrum disorder

Deep learning has shown a great promise in classifying brain disorders due to its powerful ability in learning optimal features by nonlinear transformation. However, given the high-dimension property of neuroimaging data, how to jointly exploit complementary information from multimodal neuroimaging data in deep learning is difficult. In this paper, we propose a novel multilevel convolutional neural network (CNN) fusion method that can effectively combine different types of neuroimage-derived features. Importantly, we incorporate a sequential feature selection into the CNN model to increase the feature interpretability. To evaluate our method, we classified two symptom-related brain disorders using large-sample multi-site data from 335 schizophrenia (SZ) patients and 380 autism spectrum disorder (ASD) patients within a cross-validation procedure. Brain functional networks, functional network connectivity, and brain structural morphology were employed to provide possible features. As expected, our fusion method outperformed the CNN model using only single type of features, as our method yielded higher classification accuracy (with mean accuracy >85%) and was more reliable across multiple runs in differentiating the two groups. We found that the default mode, cognitive control, and subcortical regions contributed more in their distinction. Taken together, our method provides an effective means to fuse multimodal features for the diagnosis of different psychiatric and neurological disorders.

A 16-channel loop array for in vivo macaque whole-brain imaging at 3 T

Non-human primates (NHPs) are vital models for neuroscience research. These animals have been widely used in behavioral, electrophysiological, molecular, and more recently, multimodal neuroimaging and neuro-engineering studies. Several RF coil arrays have been designed for functional, high-resolution brain magnetic resonance imaging (MRI), but few have been designed to accommodate multimodal devices. In the present study, a 16-channel array coil was constructed for brain imaging of macaques at 3 Tesla (3 T). To construct this coil, a close-fitting helmet-shaped form was designed to host 16 coil loops for whole-brain coverage. This assembly is mountable onto stereotaxic head frame bars, and the coil functions while the monkey is in the sphinx position with a clear line of vision of stimuli presented from outside of the MRI system. In addition, 4 openings were allocated in the coil housing, allowing multimodal devices to directly access visual cortical regions such as V1-V4 and MT. Coil performance was evaluated in an anesthetized macaque by quantifying and comparing signal-to-noise ratios (SNRs), noise correlations, and g-factor maps to a vendor-supplied human pediatric coil frequently used for NHP MRI. The result from in vivo experiments showed that the NHP coil was well-decoupled, had higher SNRs in cortical regions, and improved data acquisition acceleration capability compared with a vendor-supplied human pediatric coil that has been frequently used in macaque MRI studies. Furthermore, whole-brain anatomic imaging, diffusion tensor imaging and functional brain imaging have also been conducted: the details of brain anatomical structure, such as cerebellum and brainstem, can be clearly visualized in T2-SPACE images; b0 SNR calculated from b0 maps was higher than the human pediatric coil in all regions of interest (ROIs); the time-course SNR (tSNR) map calculated for GRE-EPI images demonstrates that the presented coil can be used for high-resolution functional imaging at 3 T.

Effect and cerebral mechanism of acupuncture treatment for functional constipation: study protocol for a randomized controlled clinical trial

Background

Acupuncture is effective in functional constipation (FC) treatment, but the central mechanism has not been well investigated. This trial will combine functional magnetic resonance imaging (fMRI) and positron emission tomography-computed tomography (PET-CT) to investigate the potential central mechanism of acupuncture treatment for FC.

Methods

This is a multimodal neuroimaging randomized controlled trial. In total, 140 FC patients will be randomly allocated into four groups: the verum acupuncture group; the sham acupuncture group; the PEG 4000 group; and the waiting-list group. This trial will include a two-week baseline period and a two-week treatment period. Patients will receive 10 sessions of acupuncture, sham acupuncture, PEG 4000, or no intervention during the treatment period. The stool diary, Cleveland Constipation Score (CCS), Patient Assessment of Constipation Symptom (PAC-SYM), and Patient Assessment of Constipation Quality of Life Questionnaire (PAC-QoL) will be used to assess the clinical efficacy of different interventions. The MRI and PET-CT scans will be performed to detect cerebral functional changes in 15 patients in each group at baseline and at the end of treatment/waiting. Multimodal imaging data will be associated with clinical data to investigate possible correlation between brain activity changes elicited by different interventions and symptoms improvement.

Discussion

We hypothesize that acupuncture can treat FC through normalizing the pathological alteration of the cerebral activity. The results of this trial will allow us to re-testify the therapeutic effects of acupuncture treating for FC and to investigate the potential central mechanism of acupuncture treatment for FC from direct (cerebral glucose metabolism) and indirect (contrast of oxyhemoglobin and deoxyhemoglobin) approaches.

Trial registration

Chinese Clinical Trial Registry, ChiCTR1800016658. Registered on 14 June 2018.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3410-8) contains supplementary material, which is available to authorized users.

Gray matter and white matter changes in non-demented amyotrophic lateral sclerosis patients with or without cognitive impairment: A combined voxel-based morphometry and tract-based spatial statistics whole-brain analysis

The phenotypic heterogeneity in amyotrophic lateral sclerosis (ALS) implies that patients show structural changes within but also beyond the motor cortex and corticospinal tract and furthermore outside the frontal lobes, even if frank dementia is not detected. The aim of the present study was to investigate both gray matter (GM) and white matter (WM) changes in non-demented amyotrophic lateral sclerosis (ALS) patients with or without cognitive impairment (ALS-motor and ALS-plus, respectively). Nineteen ALS-motor, 31 ALS-plus and 25 healthy controls (HC) underwent 3D-T1-weighted and 30-directional diffusion-weighted imaging on a 3 T MRI scanner. Voxel-based morphometry and tract-based spatial-statistics analysis were performed to examine GM volume (GMV) changes and WM differences in fractional anisotropy (FA), axial and radial diffusivity (AD, RD, respectively). Compared to HC, ALS-motor patients showed decreased GMV in frontal and cerebellar areas and increased GMV in right supplementary motor area, while ALS-plus patients showed diffuse GMV reduction in primary motor cortex bilaterally, frontotemporal areas, cerebellum and basal ganglia. ALS-motor patients had increased GMV in left precuneus compared to ALS-plus patients. We also found decreased FA and increased RD in the corticospinal tract bilaterally, the corpus callosum and extra-motor tracts in ALS-motor patients, and decreased FA and increased AD and RD in motor and several WM tracts in ALS-plus patients, compared to HC. Multimodal neuroimaging confirms motor and extra-motor GM and WM abnormalities in non-demented cognitively-impaired ALS patients (ALS-plus) and identifies early extra-motor brain pathology in ALS patients without cognitive impairment (ALS-motor).

Regional patterns of gray matter volume, hypometabolism, and beta-amyloid in groups at risk of Alzheimer's disease

Alzheimer's disease (AD) is characterized by the presence of β-amyloid (Aβ) deposition and neurodegeneration. To seek for signs of such pathologies, we compared regional biomarker degrees and patterns of Aβ deposition, glucose hypometabolism, and gray matter volume (GMV) reduction in 3 groups at risk of AD. In elderly carriers of the apolipoprotein E ε4 (APOE4, n = 17), patients with subjective cognitive decline (n = 16), and patients with mild cognitive impairment (n = 30), head-to-head intermodality comparisons were performed on cross-sectional structural magnetic resonance images as well as 18F-fluorodeoxyglucose and 18F-florbetapir positron emission tomography scans. In mild cognitive impairment patients, 3 distinct biomarker patterns were recovered, similarly seen in AD patients: (1) in medial temporal regions, local GMV reduction exceeded hypometabolism, (2) in temporoparietal regions, hypometabolism predominated over GMV reduction, and (3) in frontal regions, Aβ deposition exceeded GMV reduction and hypometabolism. In subjective cognitive decline patients, only pattern 1 was detected, while APOE4 carriers demonstrated only pattern 3. Our findings highlight that regional AD-like biomarker patterns may vary across different at-risk populations, potentially reflecting differential mediators of these risks.

MultiXplore: Visual exploration platform for multimodal neuroimaging data

BACKGROUND

Construction of brain functional and structural networks by neuroimaging methods facilitates inter-modal studies. These type of studies often demand exploration tools to carry out functional-structural discoveries and answer questions regarding the anatomical basis of brain networks.

NEW METHOD

This paper describes the design and development of a software module for interactive visualization and exploration of dual-modal brain networks. Our objective was to equip the user with a research tool to investigate brain connectivity matrices while visualizing relevant anatomical landmarks within a 3D volumetric view. In order to create this view, MultiXplore was designed to load data from both structural and diffusion MRI and connectivity matrices.

RESULTS

Once user starts to select desired cells through an interactive matrix unit, associated axonal fiber pathways and grey matter regions are generated and displayed. Integration and visualization of functional and structural networks in this 3D interactive framework was successfully implemented and tested.

COMPARISON WITH EXISTING METHOD(S)

MultiXplore contributes to the transition of connectivity visualization techniques from node-link format to an anatomically more realistic graphical form and assists scientists in relating connectivity matrices to their anatomical correlates. This module also benefits from additional novel functionalities to annotate and differentiate fibers in a large bundle. Unlike traditional graph displays, interactive functionality helps in the inspection and visualization of relevant structures without cluttering the scene with excessive items.

CONCLUSION

This module was designed and developed as a plugin to 3D Slicer imaging platform and is accessible for neuroimaging researchers through NITRC (http://www.nitrc.org/projects/multixplore/).

Using neuroimaging to help predict the onset of psychosis

The aim of this review is to assess the potential for neuroimaging measures to facilitate prediction of the onset of psychosis. Research in this field has mainly involved people at 'ultra-high risk' (UHR) of psychosis, who have a very high risk of developing a psychotic disorder within a few years of presentation to mental health services. The review details the key findings and developments in this area to date and examines the methodological and logistical challenges associated with making predictions in an individual subject in a clinical setting.

Length matters: Improved high field EEG-fMRI recordings using shorter EEG cables

BACKGROUND

The use of concurrent EEG-fMRI recordings has increased in recent years, allowing new avenues of medical and cognitive neuroscience research; however, currently used setups present problems with data quality and reproducibility.

NEW METHOD

We propose a compact experimental setup for concurrent EEG-fMRI at 4T and compare it to a more standard reference setup. The compact setup uses short EEG cables connecting to the amplifiers, which are placed right at the back of the head RF coil on a form-fitting extension force-locked to the patient MR bed. We compare the two setups in terms of sensitivity to MR-room environmental noise, interferences between measuring devices (EEG or fMRI), and sensitivity to functional responses in a visual stimulation paradigm.

RESULTS

The compact setup reduces the system sensitivity to both external noise and MR-induced artefacts by at least 60%, with negligible EEG noise induced from the mechanical vibrations of the cryogenic cooling compression pump.

COMPARISON WITH EXISTING METHODS

The compact setup improved EEG data quality and the overall performance of MR-artifact correction techniques. Both setups were similar in terms of the fMRI data, with higher reproducibility for cable placement within the scanner in the compact setup.

CONCLUSIONS

This improved compact setup may be relevant to MR laboratories interested in reducing the sensitivity of their EEG-fMRI experimental setup to external noise sources, setting up an EEG-fMRI workplace for the first time, or for creating a more reproducible configuration of equipment and cables. Implications for safety and ergonomics are discussed.

Recruitment of the left precentral gyrus in reading epilepsy: A multimodal neuroimaging study

Purpose

In a previous study, we investigated a 42-year-old male patient with primary reading epilepsy using continuous video-electroencephalography (EEG). Reading tasks induced left parasagittal spikes with a higher spike frequency when the phonological reading pathway was recruited compared to the lexical one. Here, we seek to localize the epileptogenic focus in the same patient as a function of reading pathway using multimodal neuroimaging.

Methods and results

The participant read irregular words and nonwords presented in a block-design paradigm during magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) recordings, all combined with EEG. Spike analyses from MEG, fNIRS, and fMRI–EEGs data revealed an epileptic focus in the left precentral gyrus, and spike localization did not differ in lexical and phonological reading.

Conclusion

This study is the first to investigate ictogenesis in reading epilepsy during both lexical and phonological reading while using three different multimodal neuroimaging techniques. The somatosensory and motor control functions of the left precentral gyrus that are congruently involved in lexical as well as phonological reading can explain the identical spike localization in both reading pathways. The concurrence between our findings in this study and those from our previous one supports the role of the left precentral gyrus in phonological output computation as well as seizure activity in a case of reading epilepsy.

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients.

Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

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We introduce common epileptogenic lesions encountered in patients with drug resistant epilepsy.

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We discuss state of the art computational techniques used to detect lesions.

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There is a need for multi-institutional studies that combine these techniques.

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Clinically validated pipelines alongside the advances in imaging and electrophysiology will improve outcomes.

Correlated patterns of neuropsychological and behavioral symptoms in frontal variant of Alzheimer disease and behavioral variant frontotemporal dementia: a comparative case study

Although the neuropathologic changes and diagnostic criteria for the neurodegenerative disorder Alzheimer's disease (AD) are well-established, the clinical symptoms vary largely. Symptomatically, frontal variant of AD (fv-AD) presents very similarly to behavioral variant frontotemporal dementia (bvFTD), which creates major challenges for differential diagnosis. Here, we report two patients who present with progressive cognitive impairment, early and prominent behavioral features, and significant frontotemporal lobe atrophy on magnetic resonance imaging, consistent with an initial diagnosis of probable bvFTD. However, multimodal functional neuroimaging revealed neuropathological data consistent with a diagnosis of probable AD for one patient (pathology distributed in the frontal lobes) and a diagnosis of probable bvFTD for the other patient (hypometabolism in the bilateral frontal lobes). In addition, the fv-AD patient presented with greater executive impairment and milder behavioral symptoms relative to the bvFTD patient. These cases highlight that recognition of these atypical syndromes using detailed neuropsychological tests, biomarkers, and multimodal neuroimaging will lead to greater accuracy in diagnosis and patient management.

Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates

Neuroimaging techniques, such as fMRI, structural MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) have uncovered evidence for widespread functional and anatomical brain abnormalities in autism spectrum disorder (ASD) suggesting it to be a system-wide neural systems disorder. Nevertheless, most previous studies have focused on examining one index of neuropathology through a single neuroimaging modality, and seldom using multiple modalities to examine the same cohort of individuals. The current study aims to bring together multiple brain imaging modalities (structural MRI, DTI, and 1H-MRS) to investigate the neural architecture in the same set of individuals (19 high-functioning adults with ASD and 18 typically developing (TD) peers). Morphometry analysis revealed increased cortical thickness in ASD participants, relative to typical controls, across the left cingulate, left pars opercularis of the inferior frontal gyrus, left inferior temporal cortex, and right precuneus, and reduced cortical thickness in right cuneus and right precentral gyrus. ASD adults also had reduced fractional anisotropy (FA) and increased radial diffusivity (RD) for two clusters on the forceps minor of the corpus callosum, revealed by DTI analyses. 1H-MRS results showed a reduction in the N-acetylaspartate/Creatine ratio in dorsal anterior cingulate cortex (dACC) in ASD participants. A decision tree classification analysis across the three modalities resulted in classification accuracy of 91.9% with FA, RD, and cortical thickness as key predictors. Examining the same cohort of adults with ASD and their TD peers, this study found alterations in cortical thickness, white matter (WM) connectivity, and neurochemical concentration in ASD. These findings underscore the potential for multimodal imaging to better inform on the neural characteristics most relevant to the disorder.

Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data

BACKGROUND

Neuroimaging machine learning studies have largely utilized supervised algorithms - meaning they require both neuroimaging scan data and corresponding target variables (e.g. healthy vs. diseased) to be successfully 'trained' for a prediction task. Noticeably, this approach may not be optimal or possible when the global structure of the data is not well known and the researcher does not have an a priori model to fit the data.

NEW METHOD

We set out to investigate the utility of an unsupervised machine learning technique; t-distributed stochastic neighbour embedding (t-SNE) in identifying 'unseen' sample population patterns that may exist in high-dimensional neuroimaging data. Multimodal neuroimaging scans from 92 healthy subjects were pre-processed using atlas-based methods, integrated and input into the t-SNE algorithm. Patterns and clusters discovered by the algorithm were visualized using a 2D scatter plot and further analyzed using the K-means clustering algorithm.

COMPARISON WITH EXISTING METHODS

t-SNE was evaluated against classical principal component analysis.

CONCLUSION

Remarkably, based on unlabelled multimodal scan data, t-SNE separated study subjects into two very distinct clusters which corresponded to subjects' gender labels (cluster silhouette index value=0.79). The resulting clusters were used to develop an unsupervised minimum distance clustering model which identified 93.5% of subjects' gender. Notably, from a neuropsychiatric perspective this method may allow discovery of data-driven disease phenotypes or sub-types of treatment responders.

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of conditions of mild cognitive impairment

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l₁-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corresponding functional connections. We applied beamformer source reconstruction to the resting state MEG recordings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was obtained for each subject, and time series were assigned to each of the regions. The fiber densities between the regions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introducing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

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We propose an anatomy-driven method for functional connectivity estimation in MEG.

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Structural prior contributes to a better representation of the functional connectivity.

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The proposed method is shown to be useful as a biomarker for classification of MCI.

Aberrant error processing in relation to symptom severity in obsessive-compulsive disorder: A multimodal neuroimaging study

BACKGROUND

Obsessive-compulsive disorder (OCD) is characterized by maladaptive repetitive behaviors that persist despite feedback. Using multimodal neuroimaging, we tested the hypothesis that this behavioral rigidity reflects impaired use of behavioral outcomes (here, errors) to adaptively adjust responses. We measured both neural responses to errors and adjustments in the subsequent trial to determine whether abnormalities correlate with symptom severity. Since error processing depends on communication between the anterior and the posterior cingulate cortex, we also examined the integrity of the cingulum bundle with diffusion tensor imaging.

METHODS

Participants performed the same antisaccade task during functional MRI and electroencephalography sessions. We measured error-related activation of the anterior cingulate cortex (ACC) and the error-related negativity (ERN). We also examined post-error adjustments, indexed by changes in activation of the default network in trials surrounding errors.

RESULTS

OCD patients showed intact error-related ACC activation and ERN, but abnormal adjustments in the post- vs. pre-error trial. Relative to controls, who responded to errors by deactivating the default network, OCD patients showed increased default network activation including in the rostral ACC (rACC). Greater rACC activation in the post-error trial correlated with more severe compulsions. Patients also showed increased fractional anisotropy (FA) in the white matter underlying rACC.

CONCLUSIONS

Impaired use of behavioral outcomes to adaptively adjust neural responses may contribute to symptoms in OCD. The rACC locus of abnormal adjustment and relations with symptoms suggests difficulty suppressing emotional responses to aversive, unexpected events (e.g., errors). Increased structural connectivity of this paralimbic default network region may contribute to this impairment.

Multimodal neuroimaging investigations of alterations to consciousness: the relationship between absence epilepsy and sleep

The link between epilepsy and sleep is well established on many levels. The focus of the current review is on recent neuroimaging investigations into the alterations of consciousness that are observed during absence seizures and the descent into sleep. Functional neuroimaging provides simultaneous cortical and subcortical recording of activity throughout the brain, allowing a detailed definition and characterization of large-scale brain networks and the interactions between them. This has led to the identification of a set of regions which collectively form the consciousness system, which includes contributions from the default mode network (DMN), ascending arousal systems, and the thalamus. Electrophysiological and neuroimaging investigations have also clearly demonstrated the importance of thalamocortical and corticothalamic networks in the evolution of sleep and absence epilepsy, two phenomena in which the subject experiences an alteration to the conscious state and a disconnection from external input. However, the precise relationship between the consciousness system, thalamocortical networks, and consciousness itself remains to be clarified. One of the fundamental challenges is to understand how distributed brain networks coordinate their activity in order to maintain and implement complex behaviors such as consciousness and how modifications to this network activity lead to alterations in consciousness. By taking into account not only the level of activation of individual brain regions but also their connectivity within specific networks and the activity and connectivity of other relevant networks, a more specific quantification of brain states can be achieved. This, in turn, may provide a more fundamental understanding of the alterations to consciousness experienced in sleep and epilepsy.

Multimodal neuroimaging in presurgical evaluation of drug-resistant epilepsy

Intracranial EEG (icEEG) monitoring is critical in epilepsy surgical planning, but it has limitations. The advances of neuroimaging have made it possible to reveal epileptic abnormalities that could not be identified previously and improve the localization of the seizure focus and the vital cortex. A frequently asked question in the field is whether non-invasive neuroimaging could replace invasive icEEG or reduce the need for icEEG in presurgical evaluation. This review considers promising neuroimaging techniques in epilepsy presurgical assessment in order to address this question. In addition, due to large variations in the accuracies of neuroimaging across epilepsy centers, multicenter neuroimaging studies are reviewed, and there is much need for randomized controlled trials (RCTs) to better reveal the utility of presurgical neuroimaging. The results of multiple studies indicate that non-invasive neuroimaging could not replace invasive icEEG in surgical planning especially in non-lesional or extratemporal lobe epilepsies, but it could reduce the need for icEEG in certain cases. With technical advances, multimodal neuroimaging may play a greater role in presurgical evaluation to reduce the costs and risks of epilepsy surgery, and provide surgical options for more patients with drug-resistant epilepsy.

The superior temporal sulcus and the N170 during face processing: single trial analysis of concurrent EEG-fMRI

Face-selective neural signals have been reliably identified using both EEG and fMRI studies. These consist of the N170 component, a neural response peaking approximately 170ms after a face is presented, and face-selective activations in the fusiform face area (FFA), the occipital face area (OFA), and the superior temporal sulcus (STS). As most neuroimaging studies examine these face-selective processes separately, the relationship between the N170 neural response and activation in the fusiform gyrus is still debated. In this study, we concurrently measured EEG and fMRI responses to upright faces, inverted faces, and objects to examine this association. We introduce a method for single-trial estimation of N170 amplitudes and correlation of the trial-by-trial variation in N170 neural responses with fMRI BOLD responses. For upright faces, BOLD responses in the right STS were negatively correlated with N170 amplitudes, showing greater activation on trials in which N170 amplitudes were larger (more negative). For inverted faces, a medial region of the fusiform gyrus (mFG) was positively correlated with N170 amplitudes, showing greater activation on trials in which N170 amplitudes were smaller (less negative). This result points to the STS as a crucial region for generating the N170 associated with face perception, and suggests that the mFG is additionally recruited for processing inverted faces, particularly on trials in which N170 is small. Despite the different time resolution of fMRI and EEG signals, our single-trial estimation and EEG-fMRI correlation method can reveal associations between activation in face-selective brain regions and neural processes at 170ms associated with face perception.

Fusing concurrent EEG-fMRI with dynamic causal modeling: application to effective connectivity during face perception

Despite the wealth of research on face perception, the interactions between core regions in the face-sensitive network of the visual cortex are not well understood. In particular, the link between neural activity in face-sensitive brain regions measured by fMRI and EEG markers of face-selective processing in the N170 component is not well established. In this study, we used dynamic causal modeling (DCM) as a data fusion approach to integrate concurrently acquired EEG and fMRI data during the perception of upright compared with inverted faces. Data features derived from single-trial EEG variability were used as contextual modulators on fMRI-derived estimates of effective connectivity between key regions of the face perception network. The overall construction of our model space was highly constrained by the effects of task and ERP parameters on our fMRI data. Bayesian model selection suggested that the occipital face area (OFA) acted as a central gatekeeper directing visual information to the superior temporal sulcus (STS), the fusiform face area (FFA), and to a medial region of the fusiform gyrus (mFG). The connection from the OFA to the STS was strengthened on trials in which N170 amplitudes to upright faces were large. In contrast, the connection from the OFA to the mFG, an area known to be involved in object processing, was enhanced for inverted faces particularly on trials in which N170 amplitudes were small. Our results suggest that trial-by-trial variation in neural activity at around 170 ms, reflected in the N170 component, reflects the relative engagement of the OFA to STS/FFA network over the OFA to mFG object processing network for face perception. Importantly, the DCMs predicted the observed data significantly better by including the modulators derived from the N170, highlighting the value of incorporating EEG-derived information to explain interactions between regions as a multi-modal data fusion method for combined EEG-fMRI.

Multimodal neuroimaging in presurgical evaluation of childhood epilepsy

In pre-surgical evaluation of pediatric epilepsy, the combined use of multiple imaging modalities for precise localization of the epileptogenic focus is a worthwhile endeavor. Advanced neuroimaging by high field Magnetic resonance imaging (MRI), diffusion tensor images, and MR spectroscopy have the potential to identify subtle lesions. ¹⁸F-FDG positron emission tomography and single photon emission tomography provide visualization of metabolic alterations of the brain in the ictal and interictal states. These techniques may have localizing value for patients which exhibit normal MRI scans. Functional MRI is helpful for non-invasively identifying areas of eloquent cortex. These advances are improving our ability to noninvasively detect epileptogenic foci which have gone undetected in the past and whose accurate localization is crucial for a favorable outcome following surgical resection.

Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG

Noninvasive functional neuroimaging, as an important tool for basic neuroscience research and clinical diagnosis, continues to face the need of improving the spatial and temporal resolution. While existing neuroimaging modalities might approach their limits in imaging capability mostly due to fundamental as well as technical reasons, it becomes increasingly attractive to integrate multiple complementary modalities in an attempt to significantly enhance the spatiotemporal resolution that cannot be achieved by any modality individually. Electrophysiological and hemodynamic/metabolic signals reflect distinct but closely coupled aspects of the underlying neural activity. Combining fMRI and EEG/MEG data allows us to study brain function from different perspectives. In this review, we start with an overview of the physiological origins of EEG/MEG and fMRI, as well as their fundamental biophysics and imaging principles, we proceed with a review of the major advances in the understanding and modeling of neurovascular coupling and in the methodologies for the fMRI-EEG/MEG simultaneous recording. Finally, we summarize important remaining issues and perspectives concerning multimodal functional neuroimaging, including brain connectivity imaging.