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.

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.

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.

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.

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.

Haptoglobin 1-1 Genotype Modulates the Association of Glycemic Control With Hippocampal Volume in Elderly Individuals With Type 2 Diabetes.

Recent evidence suggests that glycemic control is associated with cognitive function in older patients with type 2 diabetes who are carriers of the haptoglobin (Hp) 1-1 genotype compared with noncarriers. We assessed whether poor glycemic control in Hp 1-1 carriers is more strongly associated with smaller hippocampal volume than in noncarriers. Hippocampal volume was generated from high-resolution structural T1 MRI obtained for 224 participants (28 Hp 1-1 carriers [12.5%] and 196 noncarriers [87.5%]) from the Israel Diabetes and Cognitive Decline (IDCD) study, who had a mean (SD) number of years in the Maccabi Healthcare Services (MHS) registry of 8.35 (2.63) and a mean (SD) HbA1c level of 6.66 (0.73)% [49 mmol/mol]. A stronger negative association between right hippocampal volume and HbA1c was found in patients with the Hp 1-1 genotype, with a 0.032-mL decrease in right hippocampal volume per 14% increase in HbA1c (P = 0.0007) versus a 0.009-mL decrease in Hp 1-1 noncarriers (P = 0.047), after adjusting for total intracranial volume, age, sex, follow-up years in the registry, and cardiovascular factor (interaction, P = 0.025). This indicates that 29.66% of the total variance in right hippocampal volume is explained by HbA1c levels among Hp 1-1 carriers and that 3.22% is explained by HbA1c levels among Hp 1-1 noncarriers. Our results suggest that the hippocampus of Hp 1-1 carriers may be more vulnerable to the insults of poor glycemic control.