Pre-acquired Functional Connectivity Predicts Choice Inconsistency.

Economic choice theories usually assume that humans maximize utility in their choices. However, studies have shown that humans make inconsistent choices, leading to suboptimal behavior, even without context-dependent manipulations. Previous studies showed that activation in value and motor networks are associated with inconsistent choices at the moment of choice. Here, we investigated if the neural predispositions, measured before a choice task, can predict choice inconsistency in a later risky choice task. Using functional connectivity (FC) measures from resting-state functional magnetic resonance imaging (rsfMRI), derived before any choice was made, we aimed to predict subjects' inconsistency levels in a later-performed choice task. We hypothesized that rsfMRI FC measures extracted from value and motor brain areas would predict inconsistency. Forty subjects (21 females) completed a rsfMRI scan before performing a risky choice task. We compared models that were trained on FC that included only hypothesized value and motor regions with models trained on whole-brain FC. We found that both model types significantly predicted inconsistency levels. Moreover, even the whole-brain models relied mostly on FC between value and motor areas. For external validation, we used a neural network pretrained on FC matrices of 37,000 subjects and fine-tuned it on our data and again showed significant predictions. Together, this shows that the tendency for choice inconsistency is predicted by predispositions of the nervous system and that synchrony between the motor and value networks plays a crucial role in this tendency.

Neuroanatomy in virtual reality: Development and pedagogical evaluation of photogrammetry-based 3D brain models.

Anatomy studies are an essential part of medical training. The study of neuroanatomy in particular presents students with a unique challenge of three-dimensional spatial understanding. Virtual Reality (VR) has been suggested to address this challenge, yet the majority of previous reports have implemented computer-generated or imaging-based models rather than models of real brain specimens. Using photogrammetry of real human bodies and advanced editing software, we developed 3D models of a real human brain at different stages of dissection. Models were placed in a custom-built virtual laboratory, where students can walk around freely, explore, and manipulate (i.e., lift the models, rotate them for different viewpoints, etc.). Sixty participants were randomly assigned to one of three learning groups: VR, 3D printed models or read-only, and given 1 h to study the white matter tracts of the cerebrum, followed by theoretical and practical exams and a learning experience questionnaire. We show that following self-guided learning in virtual reality, students demonstrate a gain in spatial understanding and an increased satisfaction with the learning experience, compared with traditional learning approaches. We conclude that the models and virtual lab described in this work may enhance learning experience and improve learning outcomes.

Generalizing prediction of task-evoked brain activity across datasets and populations.

Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450-600 training participants and 800-1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.

Similar functional networks predict performance in both perceptual and value-based decision tasks.

There are numerous commonalities between perceptual and preferential decision processes. For instance, previous studies have shown that both of these decision types are influenced by context. Also, the same computational models can explain both. However, the neural processes and functional connections that underlie these similarities between perceptual and value-based decisions are still unclear. Hence, in the current study, we examine whether perceptual and preferential processes can be explained by similar functional networks utilizing data from the Human Connectome Project. We used resting-state functional magnetic resonance imaging data to predict performance of 2 different decision-making tasks: a value-related task (the delay discounting task) and a perceptual task (the flanker task). We then examined the existence of shared predictive-network features across these 2 decision tasks. Interestingly, we found a significant positive correlation between the functional networks, which predicted the value-based and perceptual tasks. In addition, a larger functional connectivity between visual and frontal decision brain areas was a critical feature in the prediction of both tasks. These results demonstrate that functional connections between perceptual and value-related areas in the brain are inherently related to decision-making processes across domains.

The Prediction of Brain Activity from Connectivity: Advances and Applications.

The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain-behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.

Act natural: Functional connectivity from naturalistic stimuli fMRI outperforms resting-state in predicting brain activity.

The search for an 'ideal' approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.

Predicting individual traits from unperformed tasks.

Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.

Neuromodulation of Visual Cortex Reduces the Intensity of Intrusive Memories.

Aversive events can be reexperienced as involuntary and spontaneous mental images of the event. Given that the vividness of retrieved mental images is coupled with elevated visual activation, we tested whether neuromodulation of the visual cortex would reduce the frequency and negative emotional intensity of intrusive memories. Intrusive memories of a viewed trauma film and their accompanied emotional intensity were recorded throughout 5 days. Functional connectivity, measured with resting-state functional magnetic resonance imaging prior to film viewing, was used as predictive marker for intrusions-related negative emotional intensity. Results indicated that an interaction between the visual network and emotion processing areas predicted intrusions' emotional intensity. To test the causal influence of early visual cortex activity on intrusions' emotional intensity, participants' memory of the film was reactivated by brief reminders 1 day following film viewing, followed by inhibitory 1 Hz repetitive transcranial magnetic stimulation (rTMS) over early visual cortex. Results showed that visual cortex inhibitory stimulation reduced the emotional intensity of later intrusions, while leaving intrusion frequency and explicit visual memory intact. Current findings suggest that early visual areas constitute a central node influencing the emotional intensity of intrusive memories for negative events. Potential neuroscience-driven intervention targets designed to downregulate the emotional intensity of intrusive memories are discussed.

Brain volumetric changes in the general population following the COVID-19 outbreak and lockdown.

The coronavirus disease 2019 (COVID-19) outbreak introduced unprecedented health-risks, as well as pressure on the economy, society, and psychological well-being due to the response to the outbreak. In a preregistered study, we hypothesized that the intense experience of the outbreak potentially induced stress-related brain modifications in the healthy population, not infected with the virus. We examined volumetric changes in 50 participants who underwent MRI scans before and after the COVID-19 outbreak and lockdown in Israel. Their scans were compared with those of 50 control participants who were scanned twice prior to the pandemic. Following COVID-19 outbreak and lockdown, the test group participants uniquely showed volumetric increases in bilateral amygdalae, putamen, and the anterior temporal cortices. Changes in the amygdalae diminished as time elapsed from lockdown relief, suggesting that the intense experience associated with the pandemic induced transient volumetric changes in brain regions commonly associated with stress and anxiety. The current work utilizes a rare opportunity for real-life natural experiment, showing evidence for brain plasticity following the COVID-19 global pandemic. These findings have broad implications, relevant both for the scientific community as well as the general public.

Predicting individual variability in task-evoked brain activity in schizophrenia.

What goes wrong in a schizophrenia patient's brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting-state functional connectivity and brain activity during the well-validated N-back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine-learning approach we were able to use resting-state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task-evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.

Tissue Probability Based Registration of Diffusion-Weighted Magnetic Resonance Imaging.

BACKGROUND: Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types. PURPOSE: To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs). STUDY TYPE: Retrospective longitudinal study. POPULATION: Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n = 16) and a test-set (n = 16), and 35 randomly-selected participants from the Human Connectome Project. FIELD STRENGTH/SEQUENCE: 3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence. ASSESSMENT: A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools. STATISTICAL TESTS: Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools. RESULTS: The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P < 0.05). DATA CONCLUSION: A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 1.

Widespread cortical dyslamination in epilepsy patients with malformations of cortical development.

PURPOSE: Recent research in epilepsy patients confirms our understanding of epilepsy as a network disorder with widespread cortical compromise. Here, we aimed to investigate the neocortical laminar architecture in patients with focal cortical dysplasia (FCD) and periventricular nodular heterotopia (PNH) using clinically feasible 3 T MRI. METHODS: Eighteen epilepsy patients (FCD and PNH groups; n = 9 each) and age-matched healthy controls (n = 9) underwent T1 relaxation 3 T MRI, from which component probability T1 maps were utilized to extract sub-voxel composition of 6 T1 cortical layers. Seventy-eight cortical areas of the automated anatomical labeling atlas were divided into 1000 equal-volume sub-areas for better detection of cortical abnormalities, and logistic regressions were performed to compare FCD/PNH patients with healthy controls with the T1 layers composing each sub-area as regressors. Statistical significance (p < 0.05) was determined by a likelihood-ratio test with correction for false discovery rate using Benjamini-Hochberg method. RESULTS: Widespread cortical abnormalities were observed in the patient groups. Out of 1000 sub-areas, 291 and 256 bilateral hemispheric cortical sub-areas were found to predict FCD and PNH, respectively. For each of these sub-areas, we were able to identify the T1 layer, which contributed the most to the prediction. CONCLUSION: Our results reveal widespread cortical abnormalities in epilepsy patients with FCD and PNH, which may have a role in epileptogenesis, and likely related to recent studies showing widespread structural (e.g., cortical thinning) and diffusion abnormalities in various human epilepsy populations. Our study provides quantitative information of cortical laminar architecture in epilepsy patients that can be further targeted for study in functional and neuropathological studies.

Traumatic Brain Injury Severity in a Network Perspective: A Diffusion MRI Based Connectome Study.

Traumatic brain injury (TBI) is often characterized by alterations in brain connectivity. We explored connectivity alterations from a network perspective, using graph theory, and examined whether injury severity affected structural connectivity and modulated the association between brain connectivity and cognitive deficits post-TBI. We performed diffusion imaging network analysis on chronic TBI patients, with different injury severities and healthy subjects. From both global and local perspectives, we found an effect of injury severity on network strength. In addition, regions which were considered as hubs differed between groups. Further exploration of graph measures in the determined hub regions showed that efficiency of six regions differed between groups. An association between reduced efficiency in the precuneus and nonverbal abstract reasoning deficits (calculated using actual pre-injury scores) was found in the controls but was lost in TBI patients. Our results suggest that disconnection of network hubs led to a less efficient network, which in turn may have contributed to the cognitive impairments manifested in TBI patients. We conclude that injury severity modulates the disruption of network organization, reflecting a "dose response" relationship and emphasize the role of efficiency as an important diagnostic tool to detect subtle brain injury specifically in mild TBI patients.

Alterations in Network Connectivity after Traumatic Brain Injury in Mice.

Victims of mild traumatic brain injury (mTBI) usually do not display clear morphological brain defects, but frequently have long-lasting cognitive deficits, emotional difficulties, and behavioral disturbances. In the present study we used diffusion magnetic resonance imaging (dMRI) combined with graph theory measurements to investigate the effects of mTBI on brain network connectivity. We employed a non-invasive closed-head weight-drop mouse model to produce mTBI. Mice were scanned at two time points, 24 h before the injury and either 7 or 30 days following the injury. Connectivity matrices were computed for each animal at each time point, and these were subsequently used to extract graph theory measures reflecting network integration and segregation, on both the global (i.e., whole brain) and local (i.e., single regions) levels. We found that cluster coefficient, reflecting network segregation, decreased 7 days post-injury and then returned to baseline level 30 days following the injury. Global efficiency, reflecting network integration, demonstrated opposite patterns in the left and right hemispheres, with an increase of right hemisphere efficiency at 7 days and then a decrease in efficiency following 30 days, and vice versa in the left hemisphere. These findings suggest a possible compensation mechanism acting to moderate the influence of mTBI on the global network. Moreover, these results highlight the importance of tracking the dynamic changes in mTBI over time, and the potential of structural connectivity as a promising approach for studying network integrity and pathology progression in mTBI.

Short-term plasticity following motor sequence learning revealed by diffusion magnetic resonance imaging.

Current noninvasive methods to detect structural plasticity in humans are mainly used to study long-term changes. Diffusion magnetic resonance imaging (MRI) was recently proposed as a novel approach to reveal gray matter changes following spatial navigation learning and object-location memory tasks. In the present work, we used diffusion MRI to investigate the short-term neuroplasticity that accompanies motor sequence learning. Following a 45-min training session in which participants learned to accurately play a short sequence on a piano keyboard, changes in diffusion properties were revealed mainly in motor system regions such as the premotor cortex and cerebellum. In a second learning session taking place immediately afterward, feedback was given on the timing of key pressing instead of accuracy, while participants continued to learn. This second session induced a different plasticity pattern, demonstrating the dynamic nature of learning-induced plasticity, formerly thought to require months of training in order to be detectable. These results provide us with an important reminder that the brain is an extremely dynamic structure. Furthermore, diffusion MRI offers a novel measure to follow tissue plasticity particularly over short timescales, allowing new insights into the dynamics of structural brain plasticity.

Brain structure changes induced by attention bias modification training.

Attention bias modification (ABM) therapy aims to reduce anxiety by changing threat-related attention patterns using computerized training tasks. We examined changes in brain microstructure following ABM training. Thirty-two participants were randomly assigned to one of two training conditions: active ABM training shifting attention away from threat or attention control training involving no attention modification. Participants completed six lab visits, including five training sessions and three diffusion tensor imaging scans: immediately before and after the first training session, and at the end of the training series. Indices of local and global changes in microstructure and connectivity were measured. Significant longitudinal differences in fractional anisotropy (FA) between the active and control training regimens occurred in inferior temporal cortex. Changes in FA occurred across groups within ventromedial prefrontal cortex and middle occipital gyrus. These results indicate specific effects of active ABM on brain structure. Such changes could relate to clinical effects of ABM.

Selective atrophy of the connected deepest cortical layers following small subcortical infarct.

OBJECTIVE: To explore whether in patients with chronic small subcortical infarct the cortical layers of the connected cortex are differentially affected and whether these differences correlate with clinical symptomatology. METHODS: Twenty patients with a history of chronic small subcortical infarct affecting the corticospinal tracts and 15 healthy controls were included. Connected primary motor cortex was identified with tractography starting from infarct. T1-component probability maps were calculated from T1 relaxation 3T MRI, dividing the cortex into 5 laminar gaussian classes. RESULTS: Focal cortical thinning was observed in the connected cortex and specifically only in its deepest laminar class compared to the nonaffected mirrored cortex (p < 0.001). There was loss of microstructural integrity of the affected corticospinal tract with increased mean diffusivity and decreased fractional anisotropy compared to the contralateral nonaffected tract (p ≤ 0.002). Clinical scores were correlated with microstructural damage of the corticospinal tracts and with thinning of the cortex and specifically only its deepest laminar class (p < 0.001). No differences were found in the laminar thickness pattern of the bilateral primary motor cortices or in the microstructural integrity of the bilateral corticospinal tracts in the healthy controls. CONCLUSION: Our results support the concept of secondary neurodegeneration of connected primary motor cortex after a small subcortical infarct affecting the corticospinal tract, with observations that the main cortical thinning occurs in the deepest cortex and that the clinical symptomatology is correlated with this cortical atrophy pattern. Our findings may contribute to a better understanding of structural reorganization and functional outcomes after stroke.

The Diffusion Tensor Imaging Properties of the Normal Testicles at 3 Tesla Magnetic Resonance Imaging.

RATIONALE AND OBJECTIVES: The testicles are structured in a well-defined microtubular network formation, which is expected to be reflected in high anisotropic diffusivity. However, preliminary studies reported on low values of fractional-anisotropy (FA) in the normal testicles. Our aim was to design and apply a diffusion-tensor imaging (DTI) protocol in order to elucidate the diffusivity properties of the testicles and their determining factors. MATERIALS AND METHODS: 16 healthy volunteers were prospectively scanned at 3T. The protocol included T2-weighted and DTI sequences, the latter using 24 directional diffusion gradients and 3 b-values (0, 100, and 700 s/mm2) that were separated for analysis based on the reference b-value of 0 or 100 s/mm2. Image processing of the two DTI datasets yielded the diffusion vector maps and parametric maps of their corresponding principal diffusion coefficients λ1, λ2, λ3, mean diffusivity and FA. RESULTS: The results demonstrated the feasibility of DTI to provide parametric maps of the testicles. The diffusion tensor parameters obtained using the pair of 0 and 700 s/mm2 b-values, exhibited relatively low diffusivity, with mean λ1 values of 1.36 ± 0.21 × 10-3 mm2/s and low anisotropy, with mean FA values of 0.13 ± 0.05. Analysis of DTI using the 100 and 700 s/mm2 b-values yielded a slight decrease in the diffusivity of 4%-5%, whereas FA remained similar. CONCLUSION: The diffusivity of the normal testicles is relatively slow, closed-to isotropic and hardly affected by the low b-values regime exclusion. Thus, DTI parameters of the normal testicles are neither dictated by the underlying architectural anisotropy nor microperfusion effects.

Sex beyond the genitalia: The human brain mosaic.

Whereas a categorical difference in the genitals has always been acknowledged, the question of how far these categories extend into human biology is still not resolved. Documented sex/gender differences in the brain are often taken as support of a sexually dimorphic view of human brains ("female brain" or "male brain"). However, such a distinction would be possible only if sex/gender differences in brain features were highly dimorphic (i.e., little overlap between the forms of these features in males and females) and internally consistent (i.e., a brain has only "male" or only "female" features). Here, analysis of MRIs of more than 1,400 human brains from four datasets reveals extensive overlap between the distributions of females and males for all gray matter, white matter, and connections assessed. Moreover, analyses of internal consistency reveal that brains with features that are consistently at one end of the "maleness-femaleness" continuum are rare. Rather, most brains are comprised of unique "mosaics" of features, some more common in females compared with males, some more common in males compared with females, and some common in both females and males. Our findings are robust across sample, age, type of MRI, and method of analysis. These findings are corroborated by a similar analysis of personality traits, attitudes, interests, and behaviors of more than 5,500 individuals, which reveals that internal consistency is extremely rare. Our study demonstrates that, although there are sex/gender differences in the brain, human brains do not belong to one of two distinct categories: male brain/female brain.