Delineating visual, auditory and motor regions in the human brain with functional neuroimaging: a BrainMap-based meta-analytic synthesis

The effects of childhood adversity: Two specific neural patterns

Childhood adversity (CA) is associated with an elevated risk of psychopathology across the lifespan and altered brain functions are thought to play an important role in linking CA to mental vulnerability. Previous research has proposed that CA generally influences emotion processing and particularly affects reward processing and cognitive control, yet convergent evidence for CA-related neural and functional networks underlying these processes remains to be fully understood. To investigate the impact of CA on functional brain activations, the present study performed Activation Likelihood Estimation (ALE) analyses across neuroimaging studies involving three task domains: emotion processing, cognitive control, and reward processing. ALE results revealed two significant CA-related convergences of activation in the left amygdala and insula. To better understand and characterize the functions of these ALE-derived clusters, we applied the Meta-Analytic Connectivity Modeling (MACM) approach to identify co-activation maps, and the functional decoding approach to reveal cluster-related psychological processes. Results demonstrated two distinct neural and functional networks in CA: an amygdala-centered emotion processing network and an insula-centered somatomotor processing network. These specific neural patterns indicate the effect of CA on multiple neural and functional networks engaged in sensory-motor and emotion processing functions. Our results provide insights into the neurobiological embedding associated with CA.

Impact of data processing varieties on DCM estimates of effective connectivity from task-fMRI

Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.

Predicting executive functioning from brain networks: modality specificity and age effects

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2 < 0.07, r < 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

Characterizing functional modules in the human thalamus: coactivation-based parcellation and systems-level functional decoding

The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.

Structural degeneration of the nucleus basalis of Meynert in mild cognitive impairment and Alzheimer's disease - Evidence from an MRI-based meta-analysis

Recent models of Alzheimer's disease (AD) suggest that neuropathological changes of the medial temporal lobe, especially entorhinal cortex, are preceded by degenerations of the cholinergic Nucleus basalis of Meynert (NbM). Evidence from imaging studies in humans, however, is limited. Therefore, we performed an activation-likelihood estimation meta-analysis on whole brain voxel-based morphometry (VBM) MRI data from 54 experiments and 2581 subjects in total. It revealed, compared to healthy older controls, reduced gray matter in the bilateral NbM in AD, but only limited evidence for such an effect in patients with mild cognitive impairment (MCI), which typically precedes AD. Both patient groups showed less gray matter in the amygdala and hippocampus, with hints towards more pronounced amygdala effects in AD. We discuss our findings in the context of studies that highlight the importance of the cholinergic basal forebrain in learning and memory throughout the lifespan, and conclude that they are partly compatible with pathological staging models suggesting initial and pronounced structural degenerations within the NbM in the progression of AD.

Predicting Executive Functioning from Brain Networks: Modality Specificity and Age Effects

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate to weak brain-behavior associations (R2 < .07, r < .28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

The effects of exposure to road traffic noise at school on central auditory pathway functional connectivity

As the world becomes more urbanized, more people become exposed to traffic and the risks associated with a higher exposure to road traffic noise increase. Excessive exposure to environmental noise could potentially interfere with functional maturation of the auditory brain in developing individuals. The aim of the present study was to assess the association between exposure to annual average road traffic noise (LAeq) in schools and functional connectivity of key elements of the central auditory pathway in schoolchildren. A total of 229 children from 34 representative schools in the city of Barcelona with ages between 8 and 12 years (49.2% girls) were evaluated. LAeq was obtained as the mean of 2-consecutive day measurements inside classrooms before lessons started following standard procedures to obtain an indicator of long-term road traffic noise levels. A region-of-interest functional connectivity Magnetic Resonance Imaging (MRI) approach was adopted. Functional connectivity maps were generated for the inferior colliculus, medial geniculate body of the thalamus and primary auditory cortex as key levels of the central auditory pathway. Road traffic noise in schools was significantly associated with stronger connectivity between the inferior colliculus and a bilateral thalamic region adjacent to the medial geniculate body, and with stronger connectivity between the medial geniculate body and a bilateral brainstem region adjacent to the inferior colliculus. Such a functional connectivity strengthening effect did not extend to the cerebral cortex. The anatomy of the association implicating subcortical relays suggests that prolonged road traffic noise exposure in developing individuals may accelerate maturation in the basic elements of the auditory pathway. Future research is warranted to establish whether such a faster maturation in early pathway levels may ultimately reduce the developing potential in the whole auditory system.

Predicting executive functioning from functional brain connectivity: network specificity and age effects

Healthy aging is associated with altered executive functioning (EF). Earlier studies found age-related differences in EF performance to be partially accounted for by changes in resting-state functional connectivity (RSFC) within brain networks associated with EF. However, it remains unclear which role RSFC in EF-associated networks plays as a marker for individual differences in EF performance. Here, we investigated to what degree individual abilities across 3 different EF tasks can be predicted from RSFC within EF-related, perceptuo-motor, whole-brain, and random networks separately in young and old adults. Specifically, we were interested if (i) young and old adults differ in predictability depending on network or EF demand level (high vs. low), (ii) an EF-related network outperforms EF-unspecific networks when predicting EF abilities, and (iii) this pattern changes with demand level. Both our uni- and multivariate analysis frameworks analyzing interactions between age × demand level × networks revealed overall low prediction accuracies and a general lack of specificity regarding neurobiological networks for predicting EF abilities. This questions the idea of finding markers for individual EF performance in RSFC patterns and calls for future research replicating the current approach in different task states, brain modalities, different, larger samples, and with more comprehensive behavioral measures.

Intracranial direct electrical mapping reveals the functional architecture of the human basal ganglia

The basal ganglia play a key role in integrating a variety of human behaviors through the cortico–basal ganglia–thalamo–cortical loops. Accordingly, basal ganglia disturbances are implicated in a broad range of debilitating neuropsychiatric disorders. Despite accumulating knowledge of the basal ganglia functional organization, the neural substrates and circuitry subserving functions have not been directly mapped in humans. By direct electrical stimulation of distinct basal ganglia regions in 35 refractory epilepsy patients undergoing stereoelectroencephalography recordings, we here offer currently the most complete overview of basal ganglia functional characterization, extending not only to the expected sensorimotor responses, but also to vestibular sensations, autonomic responses, cognitive and multimodal effects. Specifically, some locations identified responses weren’t predicted by the model derived from large-scale meta-analyses. Our work may mark an important step toward understanding the functional architecture of the human basal ganglia and provide mechanistic explanations of non-motor symptoms in brain circuit disorders.

Direct electrical stimulation of the basal ganglia using implanted SEEG electrodes produced a variety of motor and non-motor effects in human participants, providing insight into the functional architecture of this key brain region.

A transformer-Based neural language model that synthesizes brain activation maps from free-form text queries

Neuroimaging studies are often limited by the number of subjects and cognitive processes that can be feasibly interrogated. However, a rapidly growing number of neuroscientific studies have collectively accumulated an extensive wealth of results. Digesting this growing literature and obtaining novel insights remains to be a major challenge, since existing meta-analytic tools are constrained to keyword queries. In this paper, we present Text2Brain, an easy to use tool for synthesizing brain activation maps from open-ended text queries. Text2Brain was built on a transformer-based neural network language model and a coordinate-based meta-analysis of neuroimaging studies. Text2Brain combines a transformer-based text encoder and a 3D image generator, and was trained on variable-length text snippets and their corresponding activation maps sampled from 13,000 published studies. In our experiments, we demonstrate that Text2Brain can synthesize meaningful neural activation patterns from various free-form textual descriptions. Text2Brain is available at https://braininterpreter.com as a web-based tool for efficiently searching through the vast neuroimaging literature and generating new hypotheses.

Deficits in rate of force production during multifinger tasks are associated with cognitive status

OBJECTIVES

The multifinger force deficit (MFFD) is the decline in force generated by an individual finger as the number of fingers contributing to the action is increased. It has been proposed that as a measure of neural sufficiency rather than muscle status, it provides a means of detecting individuals at risk of cognitive decline. Age-related deficits in central neural drive exert a disproportionate impact on the rate at which force can be generated. We examined whether a MFFD derived from the maximum rate at which force is generated, is more sensitive to individual differences in cognitive status, than one calculated using the maximum level of force.

METHODS

Monotonic associations between each of two variants of the MFFD, and cognition (measured with the Montreal Cognitive Assessment), were estimated cross sectionally using generalized partial rank correlations, in which age, level of education and degree of handedness were included as covariates. The participants (n=26) were community dwelling adults aged 66-87.

RESULTS

The MFFD derived using the maximum rate of force development was negatively associated with cognitive status. The association for the MFFD based on the maximum level of force, was not statistically reliable. The associations with cognitive status obtained for both variants of the MFFD were of greater magnitude than those reported previously for standard grip strength dynamometry.

CONCLUSION

The sensitivity with which the MFFD detects risk of cognitive decline may be enhanced by using the maximum rate of force developed by each finger, rather than the maximum force generated by each finger.