An extended Human Connectome Project multimodal parcellation atlas of the human cortex and subcortical areas

Emotion, Motivation, Reasoning, and How Their Brain Systems Are Related

A unified theory of emotion and motivation is updated in which motivational states are states in which instrumental goal-directed actions are performed to obtain anticipated rewards or avoid punishers, and emotional states are states that are elicited when the (conditioned or unconditioned) instrumental reward or punisher is or is not received. This advances our understanding of emotion and motivation, for the same set of genes and associated brain systems can define the primary or unlearned rewards and punishers such as a sweet taste or pain, and the brain systems that learn to expect rewards or punishers and that therefore produce motivational and emotional states. It is argued that instrumental actions under the control of the goal are important for emotion, because they require an intervening emotional state in which an action is learned or performed to obtain the goal, that is, the reward, or to avoid the punisher. The primate including human orbitofrontal cortex computes the reward value, and the anterior cingulate cortex is involved in learning the action to obtain the goal. In contrast, when the instrumental response is overlearned and becomes a habit with stimulus-response associations, emotional states may be less involved. In another route to output, the human orbitofrontal cortex has effective connectivity to the inferior frontal gyrus regions involved in language and provides a route for declarative reports about subjective emotional states to be produced. Reasoning brain systems provide alternative strategies to obtain rewards or avoid punishers and can provide different goals for action compared to emotional systems.

Decoding the altruistic brain: An ALE meta-analysis of the functional localization of giving behaviors

Neuroimaging studies on prosocial decision-making frequently employ a costly giving paradigm, whereas there is a lack of consensus on the broader differences underlying various altruistic giving tasks. This study explores the neural substrates of altruistic giving through an ALE meta-analysis of 65 fMRI studies with 2803 participants. Altruistic giving tasks were categorized into Dictator Game (DG), Charitable Donation (CD), and Pain versus Gain (PvsG). The meta-analysis identified consistent activation in core brain regions, including the ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), and insula, which are involved in value computation, conflict monitoring, and emotional processing. Task-specific analyses revealed that the DG task activated the right dorsomedial prefrontal cortex (dmPFC) and presupplementary motor area (pre-SMA), indicating cognitive control of fairness. The CD task showed significant activation in the nucleus accumbens (NAcc) and the second visual cortex, reflecting socio-cognitive evaluation based on context and stimuli. The PvsG task uniquely activated the vmPFC and orbitofrontal cortex (OFC), suggesting rapid moral-emotional trade-offs under urgency. These findings indicate that altruistic giving is context-dependent, shaped by specific task demands. Future research should integrate computational modeling with neuroscientific data and explore individual differences and real-world applications.

Slow semantic learning in the cerebral cortex, and its relation to the hippocampal episodic memory system

A key question is how new semantic representations are formed in the human brain and how this may benefit from the hippocampal episodic memory system. Here, we describe the major effective connectivity between the hippocampal memory system and the anterior temporal lobe (ATL) semantic memory system in humans. Then, we present and model a theory of how semantic representations may be formed in the human ATL using slow associative learning in semantic attractor networks that receive inputs from the hippocampal episodic memory system. The hypothesis is that if one category of semantic representations is being processed for several seconds, then a slow short-term memory trace associative biologically plausible learning rule will enable all the components during that time to be associated together in a semantic attractor network. This benefits from the binding of components provided by the hippocampal episodic memory system. The theory is modeled in a four-layer network for view-invariant visual object recognition, followed by a semantic attractor network layer that utilizes a temporal trace associative learning rule to form semantic categories based on the inputs that occur close together in time, using inputs from the hippocampal system or from the world.

A Theory and Model of Scene Representations With Hippocampal Spatial View Cells

A theory and network model are presented of how scene representations are built by forming spatial view cells in the ventromedial visual cortical scene pathway to the hippocampus in primates including humans. Layer 1, corresponding to V1-V4, connects to Layer 2 in the retrosplenial scene area and uses competitive learning to form visual feature combination neurons for the part of the scene being fixated, a visual fixation scene patch. In Layer 3, corresponding to the parahippocampal scene area and hippocampus, the visual fixation scene patches are stitched together to form whole scene representations. This is performed with a continuous attractor network for a whole scene made from the overlapping Gaussian receptive fields of the neurons as the head rotates to view the whole scene. In addition, in Layer 3, gain modulation by gaze direction maps visual fixation scene patches to the correct part of the whole scene representation when saccades are made. Each neuron in Layer 3 is thus a spatial view cell that responds to a location in a viewed scene based on visual features in a part of the scene. The novel conceptual advances are that this theory shows how scene representations may be built in primates, including humans, based on features in spatial scenes that anchor the scene representation to the world being viewed (to allocentric, world-based, space); and how gaze direction contributes to this. This offers a revolutionary approach to understanding the spatial representations for navigation and episodic memory in primates, including humans.

Visual cortical networks for "What" and "Where" to the human hippocampus revealed with dynamical graphs

Key questions for understanding hippocampal function in memory and navigation in humans are the type and source of visual information that reaches the human hippocampus. We measured bidirectional pairwise effective connectivity with functional magnetic resonance imaging between 360 cortical regions while 956 Human Connectome Project participants viewed scenes, faces, tools, or body parts. We developed a method using deterministic dynamical graphs to define whole cortical networks and the flow in both directions between their cortical regions over timesteps after signal is applied to V1. We revealed that a ventromedial cortical visual "Where" network from V1 via the retrosplenial and medial parahippocampal scene areas reaches the hippocampus when scenes are viewed. A ventrolateral "What" visual cortical network reaches the hippocampus from V1 via V2-V4, the fusiform face cortex, and lateral parahippocampal region TF when faces/objects are viewed. There are major implications for understanding the computations of the human vs rodent hippocampus in memory and navigation: primates with their fovea and highly developed cortical visual processing networks process information about the location of faces, objects, and landmarks in viewed scenes, whereas in rodents the representations in the hippocampal system are mainly about the place where the individual is located and self-motion between places.

Direct comparisons of neural activity during placebo analgesia and nocebo hyperalgesia between humans and rats

Placebo analgesia and nocebo hyperalgesia can profoundly alter pain perception, offering critical implications for pain management. While animal models are increasingly used to explore the underlying mechanisms of these phenomena, it remains unclear whether animals experience placebo and nocebo effects in a manner comparable to humans or whether the associated neurobiological pathways are conserved across species. In this study, we introduce a novel framework for comparing brain activity between humans and rodents during placebo analgesia and nocebo hyperalgesia. Using c-Fos immunohistochemistry in rats and fMRI in humans, we examined neural activity in 70 pain-related brain regions, identifying both conserved and species-specific connectivity changes. Functional connectivity analysis, refined by pruning connections based on known anatomical pathways, revealed significant overlap in key regions, including the amygdala, anterior cingulate cortex, and nucleus accumbens, highlighting conserved circuits driving placebo and nocebo responses. This cross-species methodology offers a powerful new approach for investigating the neurobiology of pain modulation, bridging the gap between animal models and human studies. Identifying these common connections validates the use of animal models and enables preclinical researchers to focus on circuits that are conserved across species, ensuring greater translational relevance when developing new and effective treatments for pain conditions.

Triple-network model-based graph theory analysis of the effectiveness of low-dose ketamine in patients with treatment-resistant depression: two resting-state functional MRI clinical trials

BACKGROUND

Evidence suggests the crucial role of dysfunctional default mode (DMN), salience and frontoparietal (FPN) networks, collectively termed the triple network model, in the pathophysiology of treatment-resistant depression (TRD).

AIMS

Using the graph theory- and seed-based functional connectivity analyses, we attempted to elucidate the role of low-dose ketamine in the triple networks, namely the DMN, salience and FPN.

METHOD

Resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) data derived from two previous clinical trials of a single, low-dose ketamine infusion were analysed. In clinical trial 1 (Trial 1), patients with TRD were randomised to either a ketamine or normal saline group, while in clinical trial 2 (Trial 2) those patients with TRD and pronounced suicidal symptoms received a single infusion of either 0.05 mg/kg ketamine or 0.045 mg/kg midazolam. All participants underwent rs-fcMRI pre and post infusion at Day 3. Both graph theory- and seed-based functional connectivity analyses were performed independently.

RESULTS

Trial 1 demonstrated significant group-by-time effects on the degree centrality and cluster coefficient in the right posterior cingulate cortex (PCC) cortex ventral 23a and b (DMN) and the cluster coefficient in the right supramarginal gyrus perisylvian language (salience). Trial 2 found a significant group-by-time effect on the characteristic path length in the left PCC 7Am (DMN). In addition, both ketamine and normal saline infusions exerted a time effect on the cluster coefficient in the right dorsolateral prefrontal cortex a9-46v (FPN) in Trial 1.

CONCLUSIONS

These findings may support the utility of the triple-network model in elucidating ketamine's antidepressant effect. Alterations in DMN, salience and FPN function may underlie this effect.

NODDI Identifies Cognitive Associations with In Vivo Microstructural Changes in Remote Cortical Regions and Thalamocortical Pathways in Thalamic Stroke

The roles of cerebral structures distal to isolated thalamic infarcts in cognitive deficits remain unclear. We aimed to identify the in vivo microstructural characteristics of remote gray matter (GM) and thalamic pathways and elucidate their roles across cognitive domains. Patients with isolated ischemic thalamic stroke and healthy controls underwent neuropsychological assessment and magnetic resonance imaging. Neurite orientation dispersion and density imaging (NODDI) was modeled to derive the intracellular volume fraction (VFic) and orientation dispersion index. Fiber density (FD) was determined by constrained spherical deconvolution, and tensor-derived fractional anisotropy (FA) was calculated. Voxel-wise GM analysis and thalamic pathway tractography were performed. Twenty-six patients and 26 healthy controls were included. Patients exhibited reduced VFic in remote GM regions, including ipsilesional insular and temporal subregions. The microstructural metrics VFic, FD, and FA within ipsilesional thalamic pathways decreased (false discovery rate [FDR]-p < 0.05). Noteworthy associations emerged as VFic within insular cortices (ρ = -0.791 to -0.630; FDR-p < 0.05) and FD in tracts connecting the thalamus and insula (ρ = 0.830 to 0.971; FDR-p < 0.001) were closely associated with executive function. The VFic in Brodmann area 52 (ρ = -0.839; FDR-p = 0.005) and FA within its thalamic pathway (ρ = -0.799; FDR-p = 0.003) correlated with total auditory memory scores. In conclusion, NODDI revealed neurite loss in remote normal-appearing GM regions and ipsilesional thalamic pathways in thalamic stroke. Reduced cortical dendritic density and axonal density of thalamocortical tracts in specific subregions were associated with improved cognitive functions. Subacute microstructural alterations beyond focal thalamic infarcts might reflect beneficial remodeling indicating post-stroke plasticity.

Electroencephalography-guided transcranial direct current stimulation improves picture-naming performance

Transcranial direct current stimulation (tDCS) is a potential method for improving verbal function by stimulating Broca's area. Previous studies have shown the effectiveness of using functional magnetic resonance imaging (fMRI) to optimize the stimulation site, but it is unclear whether similar optimization can be achieved using scalp electroencephalography (EEG). Here, we investigated whether tDCS targeting a brain area identified by EEG can improve verbalization performance during a picture-naming task. In Experiment 1, EEG and fMRI data were acquired during a naming task with 21 participants. Comparison of EEG and fMRI data showed overlap in the highest areas of activation for 80% of the participants. In Experiment 2, tDCS was administered to 15 participants using a crossover design, with stimulation targeting the EEG-guided area, Broca's area, and sham conditions. Our findings indicated that tDCS targeting the EEG-guided area significantly improved lexical retrieval speed compared with stimulation over Broca's area and sham conditions. These results support the validity of EEG-based area identification and its use in optimizing the effects of tDCS on improving language function.

Heterogeneous Graph Representation Learning Framework for Resting-State Functional Connectivity Analysis

Brain functional connectivity analysis is important for understanding brain development and brain disorders. Recent studies have suggested that the variations of functional connectivity among multiple subnetworks are closely related to the development of diseases. However, the existing works failed to sufficiently capture the complex correlation patterns among the subnetworks and ignored the learning of heterogeneous structural information across the subnetworks. To address these issues, we formulate a new paradigm for constructing and analyzing high-order heterogeneous functional brain networks via meta-paths and propose a Heterogeneous Graph representation Learning framework (BrainHGL). Our framework consists of three key aspects: 1) Meta-path encoding for capturing rich heterogeneous topological information, 2) Meta-path interaction for exploiting complex association patterns among subnetworks and 3) Meta-path aggregation for better meta-path fusion. To the best of our knowledge, we are the first to formulate the heterogeneous brain networks for better exploiting the relationship between the subnetwork interactions and the mental disease We evaluate BrainHGL on the private center Nanjing Medical University dataset (center NMU) and the public Autism Brain Imaging Data Exchange (ABIDE) dataset. We demonstrate the effectiveness of the proposed model across various disease classification tasks, including major depression disorder (MDD), bipolar disorder (BD) and autism spectrum disorder (ASD) diagnoses. In addition, our model provides deeper insights into disease interpretability, including the critical brain subnetwork connectivities, brain regions and functional pathways. We also identified disease subtypes consistent with previous neuroscientific studies by our model, which benefits the disease identification performance. The code is available at https://github.com/IntelliDAL/Graph/BrainHGL.

Fiber length distribution characterizes the brain network maturation during early school-age

Environmental and social changes during early school age have a profound impact on brain development. However, it remains unclear how the brains of typically-developing children adjust white matter to optimize network topology during this period. This study proposes fiber length distribution as a novel nodal metric to capture the continuous maturation of brain network. We acquired dMRI data from N = 30 typically developing children in their first year of primary school and a one-year follow-up. We assessed the longitudinal changes in fiber length distribution, characterized by the median length of connected fibers for each brain region. The length median was positively correlated with degree and betweenness centrality, while negatively correlated with clustering coefficient and local efficiency. From ages 7 to 8, we observed significant decreases in length median in the temporal, superior parietal, anterior cingulate, and medial prefrontal cortices, accompanied by a reduction in long-range connections and an increase in short-range connections. Meta-analytic decoding revealed that the widespread decrease in length median occurred in regions responsible for sensory processing, whereas a more localized increase in length median was observed in regions involved in memory and cognitive control. Finally, simulation tests on healthy adults further supported that the decrease in long-range connections and increase in short-range connections contributed to enhanced network segregation and integration, respectively. Our results suggest that the dual process of short- and long-range fiber changes reflects a cost-efficient strategy for optimizing network organization during this critical developmental stage.

Dynamic reconfiguration of brain coactivation states associated with active and lecture-based learning of university physics

Academic institutions are increasingly adopting active learning methods to enhance educational outcomes. Using functional magnetic resonance imaging (fMRI), we investigated neurobiological differences between active learning and traditional lecture-based approaches in university physics education. Undergraduate students enrolled in an introductory physics course underwent an fMRI session before and after a 15-week semester. Coactivation pattern (CAP) analysis was used to examine the temporal dynamics of brain states across different cognitive contexts, including physics conceptual reasoning, physics knowledge retrieval, and rest. CAP results identified seven distinct brain states, with contributions from frontoparietal, somatomotor, and visuospatial networks. Among active learning students, physics learning was associated with increased engagement of a somatomotor network, supporting an embodied cognition framework, while lecture-based students demonstrated stronger engagement of a visuospatial network, consistent with observational learning. These findings suggest significant neural restructuring over a semester of physics learning, with different instructional approaches preferentially modulating distinct patterns of brain dynamics.

Hippocampal Discoveries: Spatial View Cells, Connectivity, and Computations for Memory and Navigation, in Primates Including Humans

Two key series of discoveries about the hippocampus are described. One is the discovery of hippocampal spatial view cells in primates. This discovery opens the way to a much better understanding of human episodic memory, for episodic memory prototypically involves a memory of where people or objects or rewards have been seen in locations "out there" which could never be implemented by the place cells that encode the location of a rat or mouse. Further, spatial view cells are valuable for navigation using vision and viewed landmarks, and provide for much richer, vision-based, navigation than the place to place self-motion update performed by rats and mice who live in dark underground tunnels. Spatial view cells thus offer a revolution in our understanding of the functions of the hippocampus in memory and navigation in humans and other primates with well-developed foveate vision. The second discovery describes a computational theory of the hippocampal-neocortical memory system that includes the only quantitative theory of how information is recalled from the hippocampus to the neocortex. It is shown how foundations for this research were the discovery of reward neurons for food reward, and non-reward, in the primate orbitofrontal cortex, and representations of value including of monetary value in the human orbitofrontal cortex; and the discovery of face identity and face expression cells in the primate inferior temporal visual cortex and how they represent transform-invariant information. This research illustrates how in order to understand a brain computation, a whole series of integrated interdisciplinary discoveries is needed to build a theory of the operation of each neural system.

A novel approach for brain connectivity using recurrent neural networks and integrated gradients

Brain connectivity is an important tool for understanding the cognitive and perceptive neural mechanisms in the neuroimaging field. Many methods for estimating effective connectivity have relied on the linear regressive model. However, the linear regression approach might fail to account for the complexity inherent in brain connectivity. Due to the recent success of deep neural networks (DNNs), regressive data are able to be predicted with high accuracy. This study aimed to develop a connectivity method using the prediction performance of a DNN model and the parameters of the model. To this end, a method is proposed that utilizes integrated gradients in a recurrent neural network model. It is an extended application of explainable artificial intelligence in the multivariate autoregressive DNN model. It would be advantageous compared to the methods using the parameters of the linear regressive model or Granger's approach referring to the difference in error between the models. The performance of the connectivity estimation was tested by simulated datasets with various conditions. The overall performance was good on multiple metrics including recall (0.94), precision (0.90), F1-score (0.92), and accuracy (0.97). Compared with other conventional methods, the proposed method is robust and precise. The proposed method also demonstrates that it can be applied to estimate the actual brain connectivity in a magnetoencephalography study. In conclusion, the connectivity method based on integrated gradients provides an accurate estimation of brain connectivity by effectively capturing complex interactions, which is validated through high performance metrics such as recall, precision, F1-score, and accuracy across multiple simulated datasets. It introduces a novel framework to combine DNN and integrated gradients and to estimate effective connectivity by the explainable AI.

Cost-benefit tradeoff mediates the transition from rule-based to memory-based processing during practice

Practice not only improves task performance but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We test the hypothesis that strategy transitions in task learning can result from decision-making guided by cost-benefit analysis. Participants learn 2 task sequences and are then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis and the model-predicted strategy transition points align with the observed behavioral slowing. Model comparisons using behavioral data show that strategy transitions are better explained by a cost-benefit analysis across alternative strategies rather than solely on memory strength. Model-guided fMRI findings suggest that the brain encodes a decision variable reflecting the cost-benefit analysis and that different strategy representations are double-dissociated. Further analyses reveal that strategy transitions are associated with activation patterns in the dorsolateral prefrontal cortex and increased pattern separation in the ventromedial prefrontal cortex. Together, these findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

Persistence of training-induced visual improvements after occipital stroke

Damage to the primary visual cortex causes homonymous visual impairments that appear to benefit from visual discrimination training. However, whether improvements persist without continued training remains to be determined and was the focus of the present study. After a baseline assessment visit, 20 participants trained twice daily in their blind-field for a minimum of six months (median=155 sessions), using a motion discrimination and integration task. At the end of training, a return study visit was used to assess recovery. Three months later, 14 of the participants returned for a third study visit to assess persistence of recovery. At each study visit, motion discrimination and integration thresholds, Humphrey visual fields, and structural MRI scans were collected. Immediately after training, all but four participants showed improvements in the trained discrimination task, and shrinkage of the perimetrically-defined visual defect. While these gains were sustained in seven out of eleven participants who improved with training, four participants lost their improvement in motion discrimination thresholds at the follow-up visit. Persistence of recovery was not related to age, time since lesion, number of training sessions performed, proportion of V1 damaged, deficit size, or optic tract degeneration measured from structural MRI scans. The present findings underscore the potential of extended visual training to induce long-term improvements in stroke-induced vision loss. However, they also highlight the need for further investigations to better understand the mechanisms driving recovery, its persistence post-training, and especially heterogeneity among participants.

Perceptual learning and neural correlates of virtual navigation in subjective cognitive decline: A pilot study

Spatial navigation deficits in age-related diseases involve brain changes affecting spatial memory and verbal cognition. Studies in blind and blindfolded individuals show that multisensory training can induce neuroplasticity through visual cortex recruitment. This proof-of-concept study introduces a digital navigation training protocol, integrating egocentric and allocentric strategies with multisensory stimulation and visual masking to enhance spatial cognition and brain connectivity in 17 individuals (mean age 57.2 years) with subjective cognitive decline. Results indicate improved spatial memory performance correlated with recruitment of the visual area 6-thalamic pathway and enhanced connectivity between memory, executive frontal areas, and default mode network (DMN) regions. Additionally, increased connectivity between allocentric and egocentric navigation areas via the retrosplenial complex (RSC) hub was observed. These findings suggest that this training has the potential to induce perceptual learning and neuroplasticity through key functional connectivity hubs, offering potential widespread cognitive benefits by enhancing critical brain network functions.

Cortical beta oscillations map to shared brain networks modulated by dopamine

Brain rhythms can facilitate neural communication for the maintenance of brain function. Beta rhythms (13-35 Hz) have been proposed to serve multiple domains of human ability, including motor control, cognition, memory, and emotion, but the overarching organisational principles remain unknown. To uncover the circuit architecture of beta oscillations, we leverage normative brain data, analysing over 30 hr of invasive brain signals from 1772 channels from cortical areas in epilepsy patients, to demonstrate that beta is the most distributed cortical brain rhythm. Next, we identify a shared brain network from beta-dominant areas with deeper brain structures, like the basal ganglia, by mapping parametrised oscillatory peaks to whole-brain functional and structural MRI connectomes. Finally, we show that these networks share significant overlap with dopamine uptake as indicated by positron emission tomography. Our study suggests that beta oscillations emerge in cortico-subcortical brain networks that are modulated by dopamine. It provides the foundation for a unifying circuit-based conceptualisation of the functional role of beta activity beyond the motor domain and may inspire an extended investigation of beta activity as a feedback signal for closed-loop neurotherapies for dopaminergic disorders.

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Processing Transition during Practice

Practice not only improves task performance but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We test the hypothesis that strategy transitions in task learning can result from decision-making guided by cost-benefit analysis. Participants learn two task sequences and are then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis, and the model-predicted strategy transition points align with the observed behavioral slowing. Model comparisons using behavioral data show that strategy transitions are better explained by a cost-benefit analysis across alternative strategies rather than solely on memory strength. Model-guided fMRI findings suggest that the brain encodes a decision variable reflecting the cost-benefit analysis and that different strategy representations are double-dissociated. Further analyses reveal that strategy transitions are associated with activation patterns in the dorsolateral prefrontal cortex and increased pattern separation in the ventromedial prefrontal cortex. Together, these findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

Selective Brain Activations and Connectivities Related to the Storage and Recall of Human Object-Location, Reward-Location, and Word-Pair Episodic Memories

Different cortical systems to the hippocampus were activated using fMRI during different types of episodic memory task. For object with scene location episodic memory, the activations were high in cortical systems involved in spatial processing, including the ventromedial visual and medial parahippocampal system. These activations for the medial parahippocampal system were higher in the right hemisphere. The activations in the face and object processing ventrolateral visual cortical stream regions FFC, PIT, V8 and TE2p were higher in the object-location in scene task than the reward-location task, and were higher in the right hemisphere. For reward-location in scene episodic memory, activations were also high in the ventromedial visual cortical spatial stream to the hippocampus, but were also selectively high in storage in key reward cortical regions (ventromedial prefrontal 10r, 10v, 10d; pregenual anterior cingulate d32, p24, p32, s32; and medial orbitofrontal cortex reward-related pOFC, 11l, OFC). For word-pair episodic memory, activations were lower in the ventromedial visual and medial parahippocampal spatial cortical stream, and were higher in language-related regions in Broca's area (44, 45, 47l), and were higher in the left hemisphere for these regions and for the many highly connected inferior frontal gyrus regions in the left hemisphere. Further, effective connectivity analyses during the episodic memory tasks showed that the direction of connectivity for these systems was from early visual cortical regions V2-V4 to the ventromedial visual cortical regions VMV1-3 and VVC for spatial scene processing; was from the pregenual anterior cingulate and orbitofrontal cortex reward systems to the hippocampal system; and was from the FFC/V8/PIT system to TE2p in the visual inferior temporal visual cortex, which has connectivity to lateral parahippocampal TF, which in turn has forward effective connectivity to the hippocampus.

The morphospace of the brain-cognition organisation

Over the past three decades, functional neuroimaging has amassed abundant evidence of the intricate interplay between brain structure and function. However, the potential anatomical and experimental overlap, independence, granularity, and gaps between functions remain poorly understood. Here, we show the latent structure of the current brain-cognition knowledge and its organisation. Our approach utilises the most comprehensive meta-analytic fMRI database (Neurosynth) to compute a three-dimensional embedding space-morphospace capturing the relationship between brain functions as we currently understand them. The space structure enables us to statistically test the relationship between functions expressed as the degree to which the characteristics of each functional map can be anticipated based on its similarities with others-the predictability index. The morphospace can also predict the activation pattern of new, unseen functions and decode thoughts and inner states during movie watching. The framework defined by the morphospace will spur the investigation of novel functions and guide the exploration of the fabric of human cognition.

Functional alterations of lateral temporal cortex for processing voice prosody in adults with autism spectrum disorder

The human auditory system includes discrete cortical patches and selective regions for processing voice information, including emotional prosody. Although behavioral evidence indicates individuals with autism spectrum disorder (ASD) have difficulties in recognizing emotional prosody, it remains understudied whether and how localized voice patches (VPs) and other voice-sensitive regions are functionally altered in processing prosody. This fMRI study investigated neural responses to prosodic voices in 25 adult males with ASD and 33 controls using voices of anger, sadness, and happiness with varying degrees of emotion. We used a functional region-of-interest analysis with an independent voice localizer to identify multiple VPs from combined ASD and control data. We observed a general response reduction to prosodic voices in specific VPs of left posterior temporal VP (TVP) and right middle TVP. Reduced cortical responses in right middle TVP were consistently correlated with the severity of autistic symptoms for all examined emotional prosodies. Moreover, representation similarity analysis revealed the reduced effect of emotional intensity in multivoxel activation patterns in left anterior superior temporal cortex only for sad prosody. These results indicate reduced response magnitudes to voice prosodies in specific TVPs and altered emotion intensity-dependent multivoxel activation patterns in adult ASDs, potentially underlying their socio-communicative difficulties.

Thin and Plain Supplementary Motor Area in Chronic Ankle Instability: A Volume- and Surface-Based Morphometric Study

CONTEXT

The supplementary motor area (SMA) is involved in the functional deficits of chronic ankle instability (CAI), but the structural basis of its abnormalities remains unclear.

OBJECTIVES

To determine the differences in volume- and surface-based morphologic features of the SMA between patients with CAI and healthy controls and the relationship between these features and the clinical features of CAI.

DESIGN

Cross-sectional study.

SETTING

Sports medicine laboratory.

PATIENTS OR OTHER PARTICIPANTS

A total of 32 patients with CAI (10 women, 22 men; age = 32.46 ± 7.51 years) and 31 healthy controls (12 women, 19 men; age = 29.70 ± 8.07 years) participated.

MAIN OUTCOME MEASURE(S)

We performed T1-weighted structural magnetic resonance imaging of participants and calculated volume- and surface-based morphologic features of SMA subregions. These subregions included anterior and posterior subdivisions of the medial portion of Brodmann area 6 (6 ma and 6 mp, respectively) and supplementary and cingulate eye fields. Between-group comparisons and correlation analysis with clinical features of CAI were performed.

RESULTS

Moderately thinner 6 mp (motor-output site; Cohen d = -0.61; 95% CI = -1.11, -0.10; P = .02) and moderately plainer 6 ma (motor-planning site; Cohen d = -0.70; 95% CI = -1.20, -0.19; P = .01) were observed in the CAI than the control group. A thinner 6 mp was correlated with lower Foot and Ankle Ability Measure Activities of Daily Living subscale scores before (r = 0.400, P = .02) and after (r = 0.449, P = .01) controlling for covariates.

CONCLUSIONS

Patients with CAI had a thinner 6 mp and a plainer 6 ma in the SMA compared with controls. The thin motor-output site of the SMA was associated with ankle dysfunction in patients. This morphologic evidence of maladaptive neuroplasticity in the SMA might promote more targeted rehabilitation of CAI.

Validation of Tenths Stereotactic Coordinates Method Using Probabilistic Tractography of the Ansa Lenticularis in Parkinson's Disease Patients

OBJECTIVE

To evaluate the accuracy of stereotactic coordinates to target the ansa lenticularis (AL) using 2 surgical planning methods, the conventional millimeter method (MM) and the normalized Tenths method (TM), assessed through individualized probabilistic tractography.

METHODS

Stereotactic targeting of the AL was assessed in 2 groups: 16 patients with Parkinson's disease and 16 healthy controls from Group 1, and 39 Parkinson's disease patients from Group 2. Structural and diffusion magnetic resonance imaging probabilistic tractography identified the AL based on the Schaltenbrand-Wahren Atlas. The MM defined stereotactic coordinates in millimeters, while the TM refined the planning by dividing the intercommissural line (AC-PC) distance into 10 equal parts, normalizing the "X," "Y," and "Z" coordinates for each patient. We subsequently compared the percentage of structural connectivity (%conn) of the AL with predefined regions of interest (ROIs), including the frontopontine-corticothalamic tracts, globus pallidus internus-ventral oral anterior, and ventral oral posterior, and quantified the streamlines in 142 brain hemispheres using the MM and TM coordinates.

RESULTS

Despite anatomical variations in intercommissural (AC-PC) line lengths between both groups (22.5 ± 2.09 mm and 24.4 ± 2.56 mm, respectively; P = 0.002), as well as differences in magnetic resonance imaging acquisition parameters, we found that the TM significantly enhanced streamline identification and %conn compared to the MM. These enhancements were noted across ROIs: frontopontine-corticothalamic and globus pallidus internus-ventral oral anterior in both hemispheres, and globus pallidus internus-ventral oral posterior in the left (P < 0.001) and right hemispheres (P = 0.03).

CONCLUSIONS

TM surpasses MM in identifying the structural connectivity between the AL and predefined ROIs, underscoring the advantages of coordinate normalization. However, variations in AC-PC line lengths and Euclidean distances between methods could lead to inaccuracies in the coordinate settings, potentially affecting the precision of structural connectivity and the efficacy of therapeutic outcomes.

A ventromedial visual cortical 'Where' stream to the human hippocampus for spatial scenes revealed with magnetoencephalography

The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1-V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1-3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical 'Where' stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T.

Processing Language Partly Shares Neural Genetic Basis with Processing Tools and Body Parts

Language is an evolutionarily salient faculty for humans that relies on a distributed brain network spanning across frontal, temporal, parietal, and subcortical regions. To understand whether the complex language network shares common or distinct genetic mechanisms, we examined the relationships between the genetic effects underlying the brain responses to language and a set of object domains that have been suggested to coevolve with language: tools, faces (indicating social), and body parts (indicating social and gesturing). Analyzing the twin datasets released by the Human Connectome Project that had functional magnetic resonance imaging data from human twin subjects (monozygotic and dizygotic) undergoing language and working memory tasks contrasting multiple object domains (198 females and 144 males for the language task; 192 females and 142 males for the working memory task), we identified a set of cortical regions in the frontal and temporal cortices and subcortical regions whose activity to language was significantly genetically influenced. The heterogeneity of the genetic effects among these language clusters was corroborated by significant differences of the human gene expression profiles (Allen Human Brain Atlas dataset). Among them, the bilateral basal ganglia (mainly dorsal caudate) exhibited a common genetic basis for language, tool, and body part processing, and the right superior temporal gyrus exhibited a common genetic basis for language and tool processing across multiple types of analyses. These results uncovered the heterogeneous genetic patterns of language neural processes, shedding light on the evolution of language and its shared origins with tools and bodily functions.

Mapping cortical excitability in the human dorsolateral prefrontal cortex

BACKGROUND

Transcranial magnetic stimulation (TMS) to the dorsolateral prefrontal cortex (dlPFC) is an effective treatment for depression, but the neural effects after TMS remains unclear. TMS paired with electroencephalography (TMS-EEG) can causally probe these neural effects. Nonetheless, variability in single pulse TMS-evoked potentials (TEPs) across dlPFC subregions, and potential artifact induced by muscle activation, necessitate detailed mapping for accurate treatment monitoring.

OBJECTIVE

To characterize early TEPs anatomically and temporally (20-50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and specifically that conditions with larger muscle artifact may exhibit lower observed EL-TEPs due to over-rejection during preprocessing. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach.

METHODS

In 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses.

RESULTS

Stimulation location significantly influenced observed EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%.

SIGNIFICANCE

EL-TEPs can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols.

A theory of hippocampal function: New developments

We develop further here the only quantitative theory of the storage of information in the hippocampal episodic memory system and its recall back to the neocortex. The theory is upgraded to account for a revolution in understanding of spatial representations in the primate, including human, hippocampus, that go beyond the place where the individual is located, to the location being viewed in a scene. This is fundamental to much primate episodic memory and navigation: functions supported in humans by pathways that build 'where' spatial view representations by feature combinations in a ventromedial visual cortical stream, separate from those for 'what' object and face information to the inferior temporal visual cortex, and for reward information from the orbitofrontal cortex. Key new computational developments include the capacity of the CA3 attractor network for storing whole charts of space; how the correlations inherent in self-organizing continuous spatial representations impact the storage capacity; how the CA3 network can combine continuous spatial and discrete object and reward representations; the roles of the rewards that reach the hippocampus in the later consolidation into long-term memory in part via cholinergic pathways from the orbitofrontal cortex; and new ways of analysing neocortical information storage using Potts networks.

Selective activations and functional connectivities to the sight of faces, scenes, body parts and tools in visual and non-visual cortical regions leading to the human hippocampus

Connectivity maps are now available for the 360 cortical regions in the Human Connectome Project Multimodal Parcellation atlas. Here we add function to these maps by measuring selective fMRI activations and functional connectivity increases to stationary visual stimuli of faces, scenes, body parts and tools from 956 HCP participants. Faces activate regions in the ventrolateral visual cortical stream (FFC), in the superior temporal sulcus (STS) visual stream for face and head motion; and inferior parietal visual (PGi) and somatosensory (PF) regions. Scenes activate ventromedial visual stream VMV and PHA regions in the parahippocampal scene area; medial (7m) and lateral parietal (PGp) regions; and the reward-related medial orbitofrontal cortex. Body parts activate the inferior temporal cortex object regions (TE1p, TE2p); but also visual motion regions (MT, MST, FST); and the inferior parietal visual (PGi, PGs) and somatosensory (PF) regions; and the unpleasant-related lateral orbitofrontal cortex. Tools activate an intermediate ventral stream area (VMV3, VVC, PHA3); visual motion regions (FST); somatosensory (1, 2); and auditory (A4, A5) cortical regions. The findings add function to cortical connectivity maps; and show how stationary visual stimuli activate other cortical regions related to their associations, including visual motion, somatosensory, auditory, semantic, and orbitofrontal cortex value-related, regions.

Spatial navigation questionnaires as a supportive diagnostic tool in early Alzheimer's disease

Impaired spatial navigation is early marker of Alzheimer's disease (AD). We examined ability of self- and informant-reported navigation questionnaires to discriminate between clinically and biomarker-defined participants, and associations of questionnaires with navigation performance, regional brain atrophy, AD biomarkers, and biomarker status. 262 participants (cognitively normal, with subjective cognitive decline, amnestic mild cognitive impairment [aMCI], and mild dementia) and their informants completed three navigation questionnaires. Navigation performance, magnetic resonance imaging volume/thickness of AD-related brain regions, and AD biomarkers were measured. Informant-reported questionnaires distinguished between cognitively normal and impaired participants, and amyloid-β positive and negative aMCI. Lower scores were associated with worse navigation performance, greater atrophy in AD-related brain regions, and amyloid-β status. Self-reported questionnaire scores did not distinguish between the groups and were weakly associated with navigation performance. Other associations were not significant. Informant-reported navigation questionnaires may be a screening tool for early AD reflecting atrophy of AD-related brain regions and AD pathology.

Unveiling the axonal connectivity between the precuneus and temporal pole: Structural evidence from the cingulum pathways

Neuroimaging studies have consistently demonstrated concurrent activation of the human precuneus and temporal pole (TP), both during resting-state conditions and various higher-order cognitive functions. However, the precise underlying structural connectivity between these brain regions remains uncertain despite significant advancements in neuroscience research. In this study, we investigated the connectivity of the precuneus and TP by employing parcellation-based fiber micro-dissections in human brains and fiber tractography techniques in a sample of 1065 human subjects and a sample of 41 rhesus macaques. Our results demonstrate the connectivity between the posterior precuneus area POS2 and the areas 35, 36, and TG of the TP via the fifth subcomponent of the cingulum (CB-V) also known as parahippocampal cingulum. This finding contributes to our understanding of the connections within the posteromedial cortices, facilitating a more comprehensive integration of anatomy and function in both normal and pathological brain processes. PRACTITIONER POINTS: Our investigation delves into the intricate architecture and connectivity patterns of subregions within the precuneus and temporal pole, filling a crucial gap in our knowledge. We revealed a direct axonal connection between the posterior precuneus (POS2) and specific areas (35, 35, and TG) of the temporal pole. The direct connections are part of the CB-V pathway and exhibit a significant association with the cingulum, SRF, forceps major, and ILF. Population-based human tractography and rhesus macaque fiber tractography showed consistent results that support micro-dissection outcomes.

Lateral septum as a possible regulatory center of maternal behaviors

The lateral septum (LS) is involved in controlling anxiety, aggression, feeding, and other motivated behaviors. Lesion studies have also implicated the LS in various forms of caring behaviors. Recently, novel experimental tools have provided a more detailed insight into the function of the LS, including the specific role of distinct cell types and their neuronal connections in behavioral regulations, in which the LS participates. This article discusses the regulation of different types of maternal behavioral alterations using the distributions of established maternal hormones such as prolactin, estrogens, and the neuropeptide oxytocin. It also considers the distribution of neurons activated in mothers in response to pups and other maternal activities, as well as gene expressional alterations in the maternal LS. Finally, this paper proposes further research directions to keep up with the rapidly developing knowledge on maternal behavioral control in other maternal brain regions.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

BACKGROUND

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

NEW METHOD

Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

RESULTS

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures.

COMPARISON WITH EXISTING METHOD

Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences.

CONCLUSIONS

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture

A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex (V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart, despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.

Two what, two where, visual cortical streams in humans

ROLLS, E. T. Two What, Two Where, Visual Cortical Streams in Humans. NEUROSCI BIOBEHAV REV 2024. Recent cortical connectivity investigations lead to new concepts about 'What' and 'Where' visual cortical streams in humans, and how they connect to other cortical systems. A ventrolateral 'What' visual stream leads to the inferior temporal visual cortex for object and face identity, and provides 'What' information to the hippocampal episodic memory system, the anterior temporal lobe semantic system, and the orbitofrontal cortex emotion system. A superior temporal sulcus (STS) 'What' visual stream utilising connectivity from the temporal and parietal visual cortex responds to moving objects and faces, and face expression, and connects to the orbitofrontal cortex for emotion and social behaviour. A ventromedial 'Where' visual stream builds feature combinations for scenes, and provides 'Where' inputs via the parahippocampal scene area to the hippocampal episodic memory system that are also useful for landmark-based navigation. The dorsal 'Where' visual pathway to the parietal cortex provides for actions in space, but also provides coordinate transforms to provide inputs to the parahippocampal scene area for self-motion update of locations in scenes in the dark or when the view is obscured.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

Causal functional maps of brain rhythms in working memory

Human working memory is a key cognitive process that engages multiple functional anatomical nodes across the brain. Despite a plethora of correlative neuroimaging evidence regarding the working memory architecture, our understanding of critical hubs causally controlling overall performance is incomplete. Causal interpretation requires cognitive testing following safe, temporal, and controllable neuromodulation of specific functional anatomical nodes. Such experiments became available in healthy humans with the advance of transcranial alternating current stimulation (tACS). Here, we synthesize findings of 28 placebo-controlled studies (in total, 1,057 participants) that applied frequency-specific noninvasive stimulation of neural oscillations and examined working memory performance in neurotypical adults. We use a computational meta-modeling method to simulate each intervention in realistic virtual brains and test reported behavioral outcomes against the stimulation-induced electric fields in different brain nodes. Our results show that stimulating anterior frontal and medial temporal theta oscillations and occipitoparietal gamma rhythms leads to significant dose-dependent improvement in working memory task performance. Conversely, prefrontal gamma modulation is detrimental to performance. Moreover, we found distinct spatial expression of theta subbands, where working memory changes followed orbitofrontal high-theta modulation and medial temporal low-theta modulation. Finally, all these results are driven by changes in working memory accuracy rather than processing time measures. These findings provide a fresh view of the working memory mechanisms, complementary to neuroimaging research, and propose hypothesis-driven targets for the clinical treatment of working memory deficits.

Changes in microstructural similarity of hippocampal subfield circuits in pathological cognitive aging

The hippocampal networks support multiple cognitive functions and may have biological roles and functions in pathological cognitive aging (PCA) and its associated diseases, which have not been explored. In the current study, a total of 116 older adults with 39 normal controls (NC) (mean age: 52.3 ± 13.64 years; 16 females), 39 mild cognitive impairment (MCI) (mean age: 68.15 ± 9.28 years, 14 females), and 38 dementia (mean age: 73.82 ± 8.06 years, 8 females) were included. The within-hippocampal subfields and the cortico-hippocampal circuits were assessed via a micro-structural similarity network approach using T1w/T2w ratio and regional gray matter tissue probability maps, respectively. An analysis of covariance was conducted to identify between-group differences in structural similarities among hippocampal subfields. The partial correlation analyses were performed to associate changes in micro-structural similarities with cognitive performance in the three groups, controlling the effect of age, sex, education, and cerebral small-vessel disease. Compared with the NC, an altered T1w/T2w ratio similarity between left CA3 and left subiculum was observed in the mild cognitive impairment (MCI) and dementia. The left CA3 was the most impaired region correlated with deteriorated cognitive performance. Using these regions as seeds for GM similarity comparisons between hippocampal subfields and cortical regions, group differences were observed primarily between the left subiculum and several cortical regions. By utilizing T1w/T2w ratio as a proxy measure for myelin content, our data suggest that the imbalanced synaptic weights within hippocampal CA3 provide a substrate to explain the abnormal firing characteristics of hippocampal neurons in PCA. Furthermore, our work depicts specific brain structural characteristics of normal and pathological cognitive aging and suggests a potential mechanism for cognitive aging heterogeneity.

Cortical thickness reveals sex differences in verbal and visuospatial memory

Although previous studies have reported the sex differences in behavior/cognition and the brain, the sex difference in the relationship between memory abilities and the underlying neural basis in the aging process remains unclear. In this study, we used a machine learning model to estimate the association between cortical thickness and verbal/visuospatial memory in females and males and then explored the sex difference of these associations based on a community-elderly cohort (n = 1153, age ranged from 50.42 to 86.67 years). We validated that females outperformed males in verbal memory, while males outperformed females in visuospatial memory. The key regions related to verbal memory in females include the medial temporal cortex, orbitofrontal cortex, and some regions around the insula. Further, those regions are more located in limbic, dorsal attention, and default-model networks, and are associated with face recognition and perception. The key regions related to visuospatial memory include the lateral prefrontal cortex, anterior cingulate gyrus, and some occipital regions. They overlapped more with dorsal attention, frontoparietal and visual networks, and were associated with object recognition. These findings imply the memory performance advantage of females and males might be related to the different memory processing tendencies and their associated network.

Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity

Background

The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates.

Methods

Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed.

Results

Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM.

Discussion

Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics.

The neural correlates of arousal: Ventral posterolateral nucleus-global transient co-activation

Arousal and awareness are two components of consciousness whose neural mechanisms remain unclear. Spontaneous peaks of global (brain-wide) blood-oxygenation-level-dependent (BOLD) signal have been found to be sensitive to changes in arousal. By contrasting BOLD signals at different arousal levels, we find decreased activation of the ventral posterolateral nucleus (VPL) during transient peaks in the global signal in low arousal and awareness states (non-rapid eye movement sleep and anesthesia) compared to wakefulness and in eyes-closed compared to eyes-open conditions in healthy awake individuals. Intriguingly, VPL-global co-activation remains high in patients with unresponsive wakefulness syndrome (UWS), who exhibit high arousal without awareness, while it reduces in rapid eye movement sleep, a state characterized by low arousal but high awareness. Furthermore, lower co-activation is found in individuals during N3 sleep compared to patients with UWS. These results demonstrate that co-activation of VPL and global activity is critical to arousal but not to awareness.

The connectivity of the human frontal pole cortex, and a theory of its involvement in exploit versus explore

The frontal pole is implicated in humans in whether to exploit resources versus explore alternatives. Effective connectivity, functional connectivity, and tractography were measured between six human frontal pole regions and for comparison 13 dorsolateral and dorsal prefrontal cortex regions, and the 360 cortical regions in the Human Connectome Project Multi-modal-parcellation atlas in 171 HCP participants. The frontal pole regions have effective connectivity with Dorsolateral Prefrontal Cortex regions, the Dorsal Prefrontal Cortex, both implicated in working memory; and with the orbitofrontal and anterior cingulate cortex reward/non-reward system. There is also connectivity with temporal lobe, inferior parietal, and posterior cingulate regions. Given this new connectivity evidence, and evidence from activations and damage, it is proposed that the frontal pole cortex contains autoassociation attractor networks that are normally stable in a short-term memory state, and maintain stability in the other prefrontal networks during stable exploitation of goals and strategies. However, if an input from the orbitofrontal or anterior cingulate cortex that expected reward, non-reward, or punishment is received, this destabilizes the frontal pole and thereby other prefrontal networks to enable exploration of competing alternative goals and strategies. The frontal pole connectivity with reward systems may be key in exploit versus explore.

Different cortical connectivities in human females and males relate to differences in strength and body composition, reward and emotional systems, and memory

Sex differences in human brain structure and function are important, partly because they are likely to be relevant to the male-female differences in behavior and in mental health. To analyse sex differences in cortical function, functional connectivity was measured in 36,531 participants (53% female) in the UK Biobank (mean age 69) using the Human Connectome Project multimodal parcellation atlas with 360 well-specified cortical regions. Most of the functional connectivities were lower in females (Bonferroni corrected), with the mean Cohen's d =  - 0.18. Removing these as covariates reduced the difference of functional connectivities for females-males from d =  - 0.18 to - 0.06. The lower functional connectivities in females were especially of somatosensory/premotor regions including the insula, opercular cortex, paracentral lobule and mid-cingulate cortex, and were correlated with lower maximum workload (r = 0.17), and with higher whole body fat mass (r =  - 0.17). But some functional connectivities were higher in females, involving especially the ventromedial prefrontal cortex and posterior cingulate cortex, and these were correlated with higher liking for some rewards such as sweet foods, higher happiness/subjective well-being, and with better memory-related functions. The main findings were replicated in 1000 individuals (532 females, mean age 29) from the Human Connectome Project. This investigation shows the cortical systems with different functional connectivity between females and males, and also provides for the first time a foundation for understanding the implications for behavior of these differences between females and males.

Mapping cortical excitability in the human dorsolateral prefrontal cortex

Objective

To characterize early TEPs anatomically and temporally (20-50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and that EL-TEP amplitude is inversely related to muscle artifact. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach.

Methods

In 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses.

Results

Stimulation location significantly influenced EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%.

Significance

Early local TMS-evoked potentials (EL-TEPs) can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols.

Highlights

Early local TMS-evoked potentials (EL-TEPs) varied significantly across the dlPFC as a function of TMS target.TMS targets with less muscle artifact had significantly larger EL-TEPs.Selection of a postero-medial target increased EL-TEPs by 102% compared to anterior targets.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

Background

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

New Method

Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

Results

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures.

Comparison with existing methods

Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences.

Conclusions

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Outcome measures for electric field modeling in tES and TMS: A systematic review and large-scale modeling study

BACKGROUND

Electric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing.

OBJECTIVES

This two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures.

METHODS

Three electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals.

RESULTS

In the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model.

CONCLUSIONS

Outcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.

Hierarchical organization of the human ventral visual streams revealed with magnetoencephalography

The hierarchical organization between 25 ventral stream visual cortical regions and 180 cortical regions was measured with magnetoencephalography using the Human Connectome Project Multimodal Parcellation atlas in 83 Human Connectome Project participants performing a visual memory task. The aim was to reveal the hierarchical organization using a whole-brain model based on generative effective connectivity with this fast neuroimaging method. V1-V4 formed a first group of interconnected regions. Especially V4 had connectivity to a ventrolateral visual stream: V8, the fusiform face cortex, and posterior inferior temporal cortex PIT. These regions in turn had effectivity connectivity to inferior temporal cortex visual regions TE2p and TE1p. TE2p and TE1p then have connectivity to anterior temporal lobe regions TE1a, TE1m, TE2a, and TGv, which are multimodal. In a ventromedial visual stream, V1-V4 connect to ventromedial regions VMV1-3 and VVC. VMV1-3 and VVC connect to the medial parahippocampal gyrus PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal PHA1-3 regions have connectivity to the hippocampal system regions the perirhinal cortex, entorhinal cortex, and hippocampus. These effective connectivities of two ventral visual cortical streams measured with magnetoencephalography provide support to the hierarchical organization of brain systems measured with fMRI, and new evidence on directionality.

Precision targeting in prediction for rTMS clinical outcome in depression: what about sgACC lateralization, metabolic connectivity, and the potential role of the cerebellum?

Predicting clinical response to repetitive transcranial magnetic stimulation (rTMS) in medication-resistant depression (MRD) has gained great importance in recent years. Mainly, the right subgenual anterior cingulate cortex (sgACC) functional connectivity has been put forward as biomarker in relation to rTMS clinical outcome. Even though the left and right sgACC may have different neurobiological functions, little is known about the possible lateralized predictive role of the sgACC in rTMS clinical outcome. In 43 right-handed antidepressant-free MRD patients, we applied a searchlight-based interregional covariance connectivity approach using the baseline 18FDG-PET scan-collected from two previous high-frequency (HF)-rTMS treatment studies delivering stimulation to the left dorsolateral prefrontal cortex (DLPFC)-and investigated whether unilateral or bilateral sgACC glucose metabolism at baseline would result in different predictive metabolic connectivity patterns. Regardless of sgACC lateralization, the weaker the sgACC seed-based baseline metabolic functional connections with the (left anterior) cerebellar areas, the significantly better the clinical outcome. However, the seed diameter seems to be crucial. Similar significant findings on sgACC metabolic connectivity with the left anterior cerebellum, also unrelated to sgACC lateralization, in relation to clinical outcome were observed when using the HCPex atlas. Although we could not substantiate that specifically right sgACC metabolic connectivity would predict HF-rTMS clinical outcome, our findings suggest considering the entire sgACC in functional connectivity predictions. Given that the interregional covariance connectivity results were significant only when using the Beck Depression Inventory (BDI-II) and not with the Hamilton Depression Rating Scale (HDRS), our sgACC metabolic connectivity observations also suggest the possible involvement of the (left) anterior cerebellum involved in higher-order cognitive processing as part of this predictive value.

Function and biochemistry of the dorsolateral prefrontal cortex during placebo analgesia: how the certainty of prior experiences shapes endogenous pain relief

Prior experiences, conditioning cues, and expectations of improvement are essential for placebo analgesia expression. The dorsolateral prefrontal cortex is considered a key region for converting these factors into placebo responses. Since dorsolateral prefrontal cortex neuromodulation can attenuate or amplify placebo, we sought to investigate dorsolateral prefrontal cortex biochemistry and function in 38 healthy individuals during placebo analgesia. After conditioning participants to expect pain relief from a placebo "lidocaine" cream, we collected baseline magnetic resonance spectroscopy (1H-MRS) at 7 Tesla over the right dorsolateral prefrontal cortex. Following this, functional magnetic resonance imaging scans were collected during which identical noxious heat stimuli were delivered to the control and placebo-treated forearm sites. There was no significant difference in the concentration of gamma-aminobutyric acid, glutamate, Myo-inositol, or N-acetylaspartate at the level of the right dorsolateral prefrontal cortex between placebo responders and nonresponders. However, we identified a significant inverse relationship between the excitatory neurotransmitter glutamate and pain rating variability during conditioning. Moreover, we found placebo-related activation within the right dorsolateral prefrontal cortex and altered functional magnetic resonance imaging coupling between the dorsolateral prefrontal cortex and the midbrain periaqueductal gray, which also correlated with dorsolateral prefrontal cortex glutamate. These data suggest that the dorsolateral prefrontal cortex formulates stimulus-response relationships during conditioning, which are then translated to altered cortico-brainstem functional relationships and placebo analgesia expression.