Multimodal Neuroimaging Data Research Articles

Recent Progress in Brain Network Models for Medical Applications: A Review.

Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions. Highlights: This paper reviewed the theoretical foundations and current medical applications of computational BNMs. Composed of neural mass models, the BNM framework allows to investigate large-scale brain dynamics behind brain diseases by linking the simulated functional signals to the empirical neurophysiological data, and has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, and predicting disease outcome. Despite that several limitations existed, one promising trend of this research field is to precisely guide clinical neuromodulation treatment based on individual BNM simulation. Conclusion: BNM carries the potential to help understand the mechanism underlying how neuropathology affects brain network dynamics, further contributing to decision-making in clinical diagnosis and treatment. Several constraints must be addressed and surmounted to pave the way for its utilization in the clinic.

Quantitative fusion of NLP, fMRI, and EEG data : A mathematical model for decoding semantic processing in the brain

This research introduces a novel mathematical model designed to integrate Natural Language Processing (NLP), functional Magnetic Resonance Imaging (fMRI), and Electroencephalography (EEG) data, aiming to decode the complex neural mechanisms of semantic processing in the human brain. By leveraging the complementary strengths of each modality—NLP’s linguistic analysis, fMRI’s spatial resolution, and EEG’s temporal precision—the model provides a groundbreaking approach to understanding how semantic information is processed across different brain regions and over time. The core of the proposed model is a dynamic, multi-layered framework that utilizes advanced statistical methods and machine learning algorithms. At its foundation, the model employs vector space representations from NLP to quantify semantic similarity and contextuality in language. These representations are then mapped onto neural activation patterns captured by fMRI and EEG, using a series of transformation matrices that are optimized through machine learning techniques. The model uniquely incorporates time-series analysis to account for the temporal dynamics of EEG data, while spatial patterns from fMRI data are analyzed through convolutional neural networks, ensuring a comprehensive integration of multimodal neuroimaging data. Key to proposed approach is the application of Bayesian inference methods to fuse these diverse data sources, allowing for the probabilistic modeling of semantic processing pathways in the brain. This enables the prediction of neural responses to linguistic stimuli with unprecedented accuracy and detail. Theoretical implications of our model suggest significant advances in understanding the neural basis of language comprehension, offering new insights into the dynamic interplay between linguistic structures and neural processes.

Central stress pathways in the development of cardiovascular disease.

Mental stress is of essential consideration when assessing cardiovascular pathophysiology in all patient populations. Substantial evidence indicates associations among stress, cardiovascular disease and aberrant brain-body communication. However, our understanding of the flow of stress information in humans, is limited, despite the crucial insights this area may offer into future therapeutic targets for clinical intervention. Key terms including mental stress, cardiovascular disease and central control, were searched in PubMed, ScienceDirect and Scopus databases. Articles indicative of heart rate and blood pressure regulation, or central control of cardiovascular disease through direct neural innervation of the cardiac, splanchnic and vascular regions were included. Focus on human neuroimaging research and the flow of stress information is described, before brain-body connectivity, via pre-motor brainstem intermediates is discussed. Lastly, we review current understandings of pathophysiological stress and cardiovascular disease aetiology. Structural and functional changes to corticolimbic circuitry encode stress information, integrated by the hypothalamus and amygdala. Pre-autonomic brain-body relays to brainstem and spinal cord nuclei establish dysautonomia and lead to alterations in baroreflex functioning, firing of the sympathetic fibres, cellular reuptake of norepinephrine and withdrawal of the parasympathetic reflex. The combined result is profoundly adrenergic and increases the likelihood of cardiac myopathy, arrhythmogenesis, coronary ischaemia, hypertension and the overall risk of future sudden stress-induced heart failure. There is undeniable support that mental stress contributes to the development of cardiovascular disease. The emerging accumulation of large-scale multimodal neuroimaging data analytics to assess this relationship promises exciting novel therapeutic targets for future cardiovascular disease detection and prevention.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer’s phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

Regional glymphatic abnormality in behavioral variant frontotemporal dementia

AbstractBackgroundThe pattern of glymphatic inflow overlaps with the geographic pattern of behavioral variant frontotemporal dementia (bvFTD). The glymphatic function has not yet been explored in bvFTD. The spatial correlation between regional glymphatic function and bvFTD remain unknown.MethodA total of 60 patients with bvFTD and 61 age‐ and sex‐matched healthy controls (HCs) were selected from two independent cohorts (discovery dataset from Xuanwu Hospital and replication dataset from the Frontotemporal Lobar Degeneration Neuroimaging Initiative study). All participant underwent neuropsychological assessment. Glymphatic measures evaluated from multimodal neuroimaging data, including choroid plexus volume, diffusion along perivascular space (DTI‐ALPS) index, and coupling between blood‐oxygen‐level‐dependent signals and cerebrospinal fluid signals (BOLD‐CSF coupling), were compared between the two groups. To evaluate glymphatic regional function, DTI‐ALPS and BOLD‐CSF coupling were further divided into anterior, middle, and posterior parts. BvFTD‐related metabolic pattern was identified using spatial covariance analysis based on [l8F]‐FDG‐PET.ResultPatients with bvFTD showed higher choroid plexus volume (P < 0.001); middle DTI‐ALPS (P < 0.001); and weaker anterior BOLD‐CSF coupling (P < 0.05) than HCs after controlling for cortical gray matter volume in both datasets. In bvFTD from the discovery dataset, the anterior DTI‐ALPS was negatively associated with the expression of the bvFTD‐related metabolic pattern (r = ‐0.51, P = 0.029) and positively related with regional standardized uptake value ratios of [l8F]‐FDG‐PET in bvFTD‐related brain regions (r = 0.44–0.60, P range: 0.008–0.039). Worse glymphatic function is associated with more severe global cognition decline, disease severity, and neuropsychiatric symptoms. In addition, anterior DTI‐ALPS was higher than posterior DTI‐ALPS (P < 0.001), whereas anterior BOLD‐CSF coupling was lower than posterior BOLD‐CSF coupling (P < 0.001).ConclusionOur findings reveal abnormal glymphatic function, especially in the anterior and middle regions of brain in bvFTD. Regional glymphatic dysfunction may contribute to the pathogenesis of bvFTD.

Distinctive modulation patterns of functional brain connectivity of the default mode network by negative social emotions are associated with Alzheimer’s disease biomarkers in community‐dwelling older adults – cross‐sectional findings from the Age‐Well trial

AbstractBackgroundRecent evidence in Alzheimer’s diseases suggests that early dysfunction of emotional processing may precede the onset of cognitive decline and dementia. Altered functional brain connectivity patterns also occur along with other brain changes many years before clinical AD symptoms.MethodWe combined baseline multimodal neuroimaging data from cognitively healthy older adults (N = 125) enrolled in the Age‐Well trial of the Medit‐Ageing study. The aim was to elucidate the relationship of five AD risk factors (amyloid‐beta depositions and hypoperfusion with AV45‐PET, hippocampal atrophy, cognition with PACC‐5, cardiovascular risk with CAIDE) with carry‐over effects of social emotions and related brain functions. Using k‐means clustering analysis with the AD risk factors, we first defined participants with higher or lower preclinical risk for AD. We then used functional Magnetic Resonance Imaging to probe functional brain connectivity in resting periods following negative socio‐emotional videos in the SoVT‐Rest paradigm.ResultParticipants with a higher preclinical AD risk showed reduced positivity bias in affect ratings and reduced connectivity patterns between control‐related frontal brain regions and emotion‐related limbic regions (amygdala). In particular, this fronto‐limbic connectivity was associated with higher cardiovascular risk on the CAIDE composite score.ConclusionWe find evidence for changes in post‐emotion functional recovery patterns in fronto‐limbic brain networks in at‐risk individuals for AD. These changes might reflect impaired emotion regulation processes.

Neuroanatomical and Functional Dissociations between Variably Present Anterior Lateral Prefrontal Sulci.

The lateral prefrontal cortex (LPFC) is an evolutionarily expanded region in humans that is critical for numerous complex functions, many of which are largely hominoid specific. Although recent work shows that the presence or absence of specific sulci in anterior LPFC is associated with cognitive performance across age groups, it is unknown whether the presence of these structures relates to individual differences in the functional organization of LPFC. To fill this gap in knowledge, we leveraged multimodal neuroimaging data from two samples encompassing 82 young adult humans (aged 22-36 years) and show that the dorsal and ventral components of the paraintermediate frontal sulcus, or pimfs, present distinct morphological (surface area), architectural (thickness and myelination), and functional (resting-state connectivity networks) properties. We further contextualize the pimfs components within classic and modern cortical parcellations. Taken together, the dorsal and ventral pimfs components mark transitions in LPFC anatomy and function, across metrics and parcellations. These results emphasize that the pimfs is a critical structure to consider when examining individual differences in the anatomical and functional organization of LPFC and suggest that future individual-level parcellations could benefit from incorporating sulcal anatomy when delineating LPFC cortical regions.

Cross-modality Neuroimage Synthesis: A Survey

Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modality neuroimaging data has proved its effectiveness in brain research. However, collecting fully aligned and paired data is expensive or even impractical, since it faces many difficulties, including high cost, long acquisition time, image corruption, and privacy issues. An alternative solution is to explore unsupervised or weakly supervised learning methods to synthesize the absent neuroimaging data. In this article, we provide a comprehensive review of cross-modality synthesis for neuroimages, from the perspectives of weakly supervised and unsupervised settings, loss functions, evaluation metrics, imaging modalities, datasets, and downstream applications based on synthesis. We begin by highlighting several opening challenges for cross-modality neuroimage synthesis. Then, we discuss representative architectures of cross-modality synthesis methods under different supervisions. This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks. Finally, we summarize the existing research findings and point out future research directions. All resources are available at https://github.com/M-3LAB/awesome-multimodal-brain-image-systhesis.

SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform.

Multimodal neuroimaging data of various brain disorders provides valuable information to understand brain function in health and disease. Various neuroimaging-based databases have been developed that mainly consist of volumetric magnetic resonance imaging (MRI) data. We present the comprehensive web-based neuroimaging platform "SWADESH" for hosting multi-disease, multimodal neuroimaging, and neuropsychological data along with analytical pipelines. This novel initiative includes neurochemical and magnetic susceptibility data for healthy and diseased conditions, acquired using MR spectroscopy (MRS) and quantitative susceptibility mapping (QSM) respectively. The SWADESH architecture also provides a neuroimaging database which includes MRI, MRS, functional MRI (fMRI), diffusion weighted imaging (DWI), QSM, neuropsychological data and associated data analysis pipelines. Our final objective is to provide a master database of major brain disease states (neurodegenerative, neuropsychiatric, neurodevelopmental, and others) and to identify characteristic features and biomarkers associated with such disorders.

Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia.

This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs. Results show (1) schizophrenia is highly multimodal and includes changes in specific networks, (2) non-linear relationships with schizophrenia are observed when interpolating among shared latent dimensions, and (3) we observe a decrease in the modularity of functional connectivity and decreased visual-sensorimotor connectivity for schizophrenia patients for the FA-sFNC and sMRI-sFNC modality pairs, respectively. Additionally, our results generally indicate decreased fractional corpus callosum anisotropy, and decreased spatial ICA map and voxel-based morphometry strength in the superior frontal lobe as found in the FA-sFNC, sMRI-FA, and sMRI-ICA modality pair clusters. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data which we hope challenges the reader to think differently about how modalities interact.

SF2Former: Amyotrophic lateral sclerosis identification from multi-center MRI data using spatial and frequency fusion transformer.

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder characterized by motor neuron degeneration. Significant research has begun to establish brain magnetic resonance imaging (MRI) as a potential biomarker to diagnose and monitor the state of the disease. Deep learning has emerged as a prominent class of machine learning algorithms in computer vision and has shown successful applications in various medical image analysis tasks. However, deep learning methods applied to neuroimaging have not achieved superior performance in classifying ALS patients from healthy controls due to insignificant structural changes correlated with pathological features. Thus, a critical challenge in deep models is to identify discriminative features from limited training data. To address this challenge, this study introduces a framework called SF2Former, which leverages the power of the vision transformer architecture to distinguish ALS subjects from the control group by exploiting the long-range relationships among image features. Additionally, spatial and frequency domain information is combined to enhance the network's performance, as MRI scans are initially captured in the frequency domain and then converted to the spatial domain. The proposed framework is trained using a series of consecutive coronal slices and utilizes pre-trained weights from ImageNet through transfer learning. Finally, a majority voting scheme is employed on the coronal slices of each subject to generate the final classification decision. The proposed architecture is extensively evaluated with multi-modal neuroimaging data (i.e., T1-weighted, R2*, FLAIR) using two well-organized versions of the Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The experimental results demonstrate the superiority of the proposed strategy in terms of classification accuracy compared to several popular deep learning-based techniques.

Pathology steered stratification network for subtype identification in Alzheimer's disease.

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms. We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. The proposed PSSN (i) reduces neuroimage data to low-dimensional feature vectors, (ii) combines AT[N]-Net based on real pathological pathways, (iii) predicts long-term biomarker trajectories, and (iv) stratifies subjects into fine-grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.

Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial

We present data from the Heart Rate Variability and Emotion Regulation (HRV-ER) randomized clinical trial testing effects of HRV biofeedback. Younger (N = 121) and older (N = 72) participants completed baseline magnetic resonance imaging (MRI) including T1-weighted, resting and emotion regulation task functional MRI (fMRI), pulsed continuous arterial spin labeling (PCASL), and proton magnetic resonance spectroscopy (1H MRS). During fMRI scans, physiological measures (blood pressure, pulse, respiration, and end-tidal CO2) were continuously acquired. Participants were randomized to either increase heart rate oscillations or decrease heart rate oscillations during daily sessions. After 5 weeks of HRV biofeedback, they repeated the baseline measurements in addition to new measures (ultimatum game fMRI, training mimicking during blood oxygen level dependent (BOLD) and PCASL fMRI). Participants also wore a wristband sensor to estimate sleep time. Psychological assessment comprised three cognitive tests and ten questionnaires related to emotional well-being. A subset (N = 104) provided plasma samples pre- and post-intervention that were assayed for amyloid and tau. Data is publicly available via the OpenNeuro data sharing platform.

Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models.

Alzheimer's disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience.

A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data.

Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are widely used to reveal hidden patterns from different brain imaging modalities. However, these findings are bounded by the limitation of each modality. In addition, the lack of validity of current psychosis nosology created more complications in understanding biomarkers. In this study, we introduced a deep convolutional framework to classify and identify label noises using structural and functional magnetic resonance imaging data. We applied our method to functional and structural MRI data from a schizophrenia dataset and evaluated the model's performance in a cross-validated form. In addition, we introduced a noise criterion to distinguish a potentially noisy subject for each modality. Our results show the learned model using resting-state functional MRI data is more informative and has higher performance in comparison with structural MRI data. Lastly, based on the noise level, we investigated potential borderline subjects as possible subtypes and made a statistical analysis to distinguish differences between resting-state static functional connectivity features.Clinical Relevance- Results show schizophrenia patients are separable from the healthy control group based on their neuroimaging data and resting-state functional MRI data is more informative than structural MRI data and hence contains less label noise.

The Variegation of Human Brain Vulnerability to Rare Genetic Disorders and Convergence With Behaviorally Defined Disorders

BackgroundDiverse gene dosage disorders (GDDs) increase risk for psychiatric impairment, but characterization of GDD effects on the human brain has so far been piecemeal, with few simultaneous analyses of multiple brain features across different GDDs. MethodsHere, through multimodal neuroimaging of 3 aneuploidy syndromes (XXY [total n = 191, 92 control participants], XYY [total n = 81, 47 control participants], and trisomy 21 [total n = 69, 41 control participants]), we systematically mapped the effects of supernumerary X, Y, and chromosome 21 dosage across a breadth of 15 different macrostructural, microstructural, and functional imaging–derived phenotypes (IDPs). ResultsThe results revealed considerable diversity in cortical changes across GDDs and IDPs. This variegation of IDP change underlines the limitations of studying GDD effects unimodally. Integration across all IDP change maps revealed highly distinct architectures of cortical change in each GDD along with partial coalescence onto a common spatial axis of cortical vulnerability that is evident in all 3 GDDs. This common axis shows strong alignment with shared cortical changes in behaviorally defined psychiatric disorders and is enriched for specific molecular and cellular signatures. ConclusionsUse of multimodal neuroimaging data in 3 aneuploidies indicates that different GDDs impose unique fingerprints of change in the human brain that differ widely depending on the imaging modality that is being considered. Embedded in this variegation is a spatial axis of shared multimodal change that aligns with shared brain changes across psychiatric disorders and therefore represents a major high-priority target for future translational research in neuroscience.

BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease.

Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer's Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance.

Neurobiology of pregnancy and the postpartum period in the common marmoset

Preclinical data suggest pregnancy is associated with neurobiological changes in brain regions relevant to maternal behavior. Unfortunately, there is an absence of basic neurobiological information about the effects of pregnancy and the postpartum on the human brain. Moreover, we lack relevant studies in non-human primates that track changes in brain systems across pregnancy and parturition and their associations with maternal behavior. To bridge this gap, we examined the potential relationship between brain structure and function, and maternal behavior across pregnancy in the common marmoset (Callithrix jacchus). Eight nulliparous females (4 pregnant; 4 non-pregnant controls) were studied at four time points: pre-pregnancy baseline, gestation 1 (50-70days), gestation 2 (105-125days), and postpartum (3-14days). Infant-directed behavior was assessed using a custom-built T-Maze designed to assess responsiveness towards two infants. The number of times the female approached either infant and the overall pattern of movement was tracked using EthoVision and compared to non-pregnant multiparous and postpartum controls. Structural (T1-weighted and DTI) and functional (awake resting-state) MRI scans were collected on a 7 T Bruker scanner. Our data thus far show that among nulliparous females, infant-directed behavior decreased across gestation. During the postpartum period, the now primiparous females showed behavior equivalent to postpartum controls. Our preliminary observations support previous findings of infant avoidance in nulliparous females prior to parturition and increased infant responsiveness during the postpartum. Future analysis of our multimodal neuroimaging data will help elucidate the relationship between pregnancy and the postpartum, maternal behavior, and putative changes in the brain.

Prediction of cognitive performance differences in older age from multimodal neuroimaging data.

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85years) from the 1000BRAINS study. Prediction potential was examined for each modalityand all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.