Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing

Quality over quantity: powering neuroimaging samples in psychiatry

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.

A dose-ranging study of the physiological and self-reported effects of repeated, rapid infusion of remifentanil in people with opioid use disorder and physical dependence on fentanyl

RATIONALE

Understanding mechanisms of drug use decisions will inform the development of treatments for opioid use disorder (OUD). Decision-making experiments using neurobehavioral approaches require many trials or events of interest for statistical analysis, but the pharmacokinetics of most opioids limit dosing in humans.

OBJECTIVES

This experiment characterized the effects of repeated infusions of the ultra-short acting opioid remifentanil in people with OUD and physical opioid dependence.

METHODS

An inpatient study using a within-subjects, single-blind, escalating, within-session, pre-post design was conducted. Seven (3 female) subjects were maintained on oral oxycodone (40-60 mg, 4x/day = 160-240 total mg/day) for seven days prior to the dose-ranging session. Subjects received infusions of three ascending remifentanil doses (0.03, 0.1, 0.3 mcg/kg/infusion in 2 subjects; 0.1, 0.3, 1.0 mcg/kg/infusion in 5 subjects) every minute for 40 min per dose, with infusions administered over 5 s to model naturalistic delivery rates. End tidal carbon dioxide, respiration rate, oxygen saturation (SpO2) and heart rate were measured continuously. Blood pressure (BP), pupil diameter and self-reported drug effects were measured every 5 min.

RESULTS

Pupil diameter, SpO2 and systolic BP decreased, and ratings on prototypic subjective effects questionnaire items increased, as a function of remifentanil dose. The number of infusions held because of sedation or physiological parameters exceeding predetermined cutoffs also increased with dose.

CONCLUSIONS

This experiment established doses and procedures for the safe delivery of rapid, repeated remifentanil infusions to individuals with OUD and physical fentanyl dependence, which can be applied to the mechanistic study of opioid use decisions.

Two common and distinct forms of variation in human functional brain networks

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.

An executive-functions-based reading training enhances sensory-motor systems integration during reading fluency in children with dyslexia

The Simple View of Reading model suggests that intact language processing and word decoding lead to proficient reading comprehension, with recent studies pointing at executive functions as an important component contributing to reading proficiency. Here, we aimed to determine the underlying mechanism(s) for these changes. Participants include 120 8- to 12-year-old children (n = 55 with dyslexia, n = 65 typical readers) trained on an executive functions-based reading program, including pre/postfunctional MRI and behavioral data collection. Across groups, improved word reading was related to stronger functional connections within executive functions and sensory networks. In children with dyslexia, faster and more accurate word reading was related to stronger functional connections within and between sensory networks. These results suggest greater synchronization of brain systems after the intervention, consistent with the "neural noise" hypothesis in children with dyslexia and support the consideration of including executive functions as part of the Simple View of Reading model.

Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Bayesian longitudinal tensor response regression for modeling neuroplasticity

A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel-wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long-term increases in brain activity, the intention treatment produced predominantly short-term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel-wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power.

Cognitive deficits and altered functional brain network organization in pediatric brain tumor patients

Survivors of pediatric brain tumors experience significant cognitive deficits from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Resting state functional magnetic resonance imaging allows for the study of the spontaneous fluctuations in bulk neural activity, providing insight into brain organization and function. Here, we evaluated cognitive performance and functional network architecture in pediatric brain tumor patients. Forty-nine patients (7-18 years old) with a primary brain tumor diagnosis underwent resting state imaging during regularly scheduled clinical visits. All patients were tested with a battery of cognitive assessments. Extant data from 139 typically developing children were used as controls. We found that obtaining high-quality imaging data during routine clinical scanning was feasible. Functional network organization was significantly altered in patients, with the largest disruptions observed in patients who received propofol sedation. Awake patients demonstrated significant decreases in association network segregation compared to controls. Interestingly, there was no difference in the segregation of sensorimotor networks. With a median follow-up of 3.1 years, patients demonstrated cognitive deficits in multiple domains of executive function. Finally, there was a weak correlation between decreased default mode network segregation and poor picture vocabulary score. Future work with longer follow-up, longitudinal analyses, and a larger cohort will provide further insight into this potential predictor.

Neural Mechanisms Underlying Trust to Friends, Community Members, and Unknown Peers in Adolescence

Trust plays an important role during adolescence for developing social relations. Although prior developmental studies give us insight into adolescents' development of differentiation between close (e.g., friends) and unknown (e.g., unknown peers) targets in trust choices, less is known about the development of trust to societal targets (e.g., members of a community organization) and its underlying neural mechanisms. Using a modified version of the Trust Game, our preregistered fMRI study examined the underlying neural mechanisms of trust to close (friend), societal (community member), and unknown others (unknown peer) during adolescence in 106 participants (aged 12-23 years). Adolescents showed most trust to friends, less trust to community members, and the least trust to unknown peers. Neural results show that target differentiation in adolescents' trust behavior is associated with activity in social brain regions implicated during mentalizing, reward processing, and cognitive control. Recruitment of the medial prefrontal cortex (mPFC) and OFC was higher for closer targets (i.e., friend and community member). For the mPFC, this effect was most pronounced during no trust choices. Trust to friends was additionally associated with increased activity in the precuneus and bilateral temporal parietal junction. In contrast, bilateral dorsolateral prefrontal cortex and anterior cingulate cortex were most active for trust to unknown peers. The mPFC showed increased activity with age and consistent relations with individual differences in feeling needed/useful.

Substructure of the brain's Cingulo-Opercular network

The Cingulo-Opercular network (CON) is an executive network of the human brain that regulates actions. CON is composed of many widely distributed cortical regions that are involved in top-down control over both lower-level (i.e., motor) and higher-level (i.e., cognitive) functions, as well as in processing of painful stimuli. Given the topographical and functional heterogeneity of the CON, we investigated whether subnetworks within the CON support separable aspects of action control. Using precision functional mapping (PFM) in 15 participants with > 5 hours of resting state functional connectivity (RSFC) and task data, we identified three anatomically and functionally distinct CON subnetworks within each individual. These three distinct subnetworks were linked to Decisions, Actions, and Feedback (including pain processing), respectively, in convergence with a meta-analytic task database. These Decision, Action and Feedback subnetworks represent pathways by which the brain establishes top-down goals, transforms those goals into actions, implemented as movements, and processes critical action feedback such as pain.

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed 'variants' in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

Mapping cortical activations underlying covert and overt language production using high-density diffuse optical tomography

Gold standard neuroimaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and more recently electrocorticography (ECoG) have provided profound insights regarding the neural mechanisms underlying the processing of language, but they are limited in applications involving naturalistic language production especially in developing brains, during face-to-face dialogues, or as a brain-computer interface. High-density diffuse optical tomography (HD-DOT) provides high-fidelity mapping of human brain function with comparable spatial resolution to that of fMRI but in a silent and open scanning environment similar to real-life social scenarios. Therefore, HD-DOT has potential to be used in naturalistic settings where other neuroimaging modalities are limited. While HD-DOT has been previously validated against fMRI for mapping the neural correlates underlying language comprehension and covert (i.e., "silent") language production, HD-DOT has not yet been established for mapping the cortical responses to overt (i.e., "out loud") language production. In this study, we assessed the brain regions supporting a simple hierarchy of language tasks: silent reading of single words, covert production of verbs, and overt production of verbs in normal hearing right-handed native English speakers (n = 33). First, we found that HD-DOT brain mapping is resilient to movement associated with overt speaking. Second, we observed that HD-DOT is sensitive to key activations and deactivations in brain function underlying the perception and naturalistic production of language. Specifically, statistically significant results were observed that show recruitment of regions in occipital, temporal, motor, and prefrontal cortices across all three tasks after performing stringent cluster-extent based thresholding. Our findings lay the foundation for future HD-DOT studies of imaging naturalistic language comprehension and production during real-life social interactions and for broader applications such as presurgical language assessment and brain-machine interfaces.

Thalamo-cortical and cerebello-cortical functional connectivity in development

The thalamus is a critical relay center for neural pathways involving sensory, motor, and cognitive functions, including cortico-striato-thalamo-cortical and cortico-ponto-cerebello-thalamo-cortical loops. Despite the importance of these circuits, their development has been understudied. One way to investigate these pathways in human development in vivo is with functional connectivity MRI, yet few studies have examined thalamo-cortical and cerebello-cortical functional connectivity in development. Here, we used resting-state functional connectivity to measure functional connectivity in the thalamus and cerebellum with previously defined cortical functional networks in 2 separate data sets of children (7-12 years old) and adults (19-40 years old). In both data sets, we found stronger functional connectivity between the ventral thalamus and the somatomotor face cortical functional network in children compared with adults, extending previous cortico-striatal functional connectivity findings. In addition, there was more cortical network integration (i.e. strongest functional connectivity with multiple networks) in the thalamus in children than in adults. We found no developmental differences in cerebello-cortical functional connectivity. Together, these results suggest different maturation patterns in cortico-striato-thalamo-cortical and cortico-ponto-cerebellar-thalamo-cortical pathways.

The confound of hemodynamic response function variability in human resting-state functional MRI studies

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.

From correlation to communication: Disentangling hidden factors from functional connectivity changes

While correlations in the BOLD fMRI signal are widely used to capture functional connectivity (FC) and its changes across contexts, its interpretation is often ambiguous. The entanglement of multiple factors including local coupling of two neighbors and nonlocal inputs from the rest of the network (affecting one or both regions) limits the scope of the conclusions that can be drawn from correlation measures alone. Here we present a method of estimating the contribution of nonlocal network input to FC changes across different contexts. To disentangle the effect of task-induced coupling change from the network input change, we propose a new metric, "communication change," utilizing BOLD signal correlation and variance. With a combination of simulation and empirical analysis, we demonstrate that (1) input from the rest of the network accounts for a moderate but significant amount of task-induced FC change and (2) the proposed "communication change" is a promising candidate for tracking the local coupling in task context-induced change. Additionally, when compared to FC change across three different tasks, communication change can better discriminate specific task types. Taken together, this novel index of local coupling may have many applications in improving our understanding of local and widespread interactions across large-scale functional networks.

Neural correlates of changing food choices while bypassing values

Current theories suggest that altering choices requires value modification. To investigate this, normal-weight female participants' food choices and values were tested before and after an approach-avoidance training (AAT), while neural activity was recorded during the choice task using functional magnetic resonance imaging (fMRI). During AAT, participants consistently approached low- while avoiding high-calorie food cues. AAT facilitated low-calorie food choices, leaving food values unchanged. Instead, we observed a shift in indifference points, indicating the decreased contribution of food values in food choices. Training-induced choice shifts were associated with increased activity in the posterior cingulate cortex (PCC). In contrast, the medial PFC activity was not changed. Additionally, PCC grey matter density predicted individual differences in training-induced functional changes, suggesting anatomic predispositions to training impact. Our findings demonstrate neural mechanisms underlying choice modulation independent of valuation-related processes, with substantial theoretical significance for decision-making frameworks and translational implications for health-related decisions resilient to value shifts.

Modeling the relationship between neuronal activity and the BOLD signal: contributions from astrocyte calcium dynamics

Functional magnetic resonance imaging relies on the coupling between neuronal and vascular activity, but the mechanisms behind this coupling are still under discussion. Recent experimental evidence suggests that calcium signaling may play a significant role in neurovascular coupling. However, it is still controversial where this calcium signal is located (in neurons or elsewhere), how it operates and how relevant is its role. In this paper we introduce a biologically plausible model of the neurovascular coupling and we show that calcium signaling in astrocytes can explain main aspects of the dynamics of the coupling. We find that calcium signaling can explain so-far unrelated features such as the linear and non-linear regimes, the negative vascular response (undershoot) and the emergence of a (calcium-driven) Hemodynamic Response Function. These features are reproduced here for the first time by a single model of the detailed neuronal-astrocyte-vascular pathway. Furthermore, we analyze how information is coded and transmitted from the neuronal to the vascular system and we predict that frequency modulation of astrocytic calcium dynamics plays a key role in this process. Finally, our work provides a framework to link neuronal activity to the BOLD signal, and vice-versa, where neuronal activity can be inferred from the BOLD signal. This opens new ways to link known alterations of astrocytic calcium signaling in neurodegenerative diseases (e.g. Alzheimer's and Parkinson's diseases) with detectable changes in the neurovascular coupling.

Bilateral increase in MEG planar gradients prior to saccade onset

Every time we move our eyes, the retinal locations of objects change. To distinguish the changes caused by eye movements from actual external motion of the objects, the visual system is thought to anticipate the consequences of eye movements (saccades). Single neuron recordings have indeed demonstrated changes in receptive fields before saccade onset. Although some EEG studies with human participants have also demonstrated a pre-saccadic increased potential over the hemisphere that will process a stimulus after a saccade, results have been mixed. Here, we used magnetoencephalography to investigate the timing and lateralization of visually evoked planar gradients before saccade onset. We modelled the gradients from trials with both a saccade and a stimulus as the linear combination of the gradients from two conditions with either only a saccade or only a stimulus. We reasoned that any residual gradients in the condition with both a saccade and a stimulus must be uniquely linked to visually-evoked neural activity before a saccade. We observed a widespread increase in residual planar gradients. Interestingly, this increase was bilateral, showing activity both contralateral and ipsilateral to the stimulus, i.e. over the hemisphere that would process the stimulus after saccade offset. This pattern of results is consistent with predictive pre-saccadic changes involving both the current and the future receptive fields involved in processing an attended object, well before the start of the eye movement. The active, sensorimotor coupling of vision and the oculomotor system may underlie the seamless subjective experience of stable and continuous perception.

Evaluation of T2-prepared blood oxygenation level dependent functional magnetic resonance imaging with an event-related task: Hemodynamic response function and reproducibility

T2-prepared (T2prep) blood oxygenation level dependent (BOLD) functional MRI (fMRI) is an alternative fMRI approach developed to mitigate the susceptibility artifacts that are typically observed in brain regions near air-filled cavities, bleeding and calcification, and metallic objects in echo-planar-imaging (EPI) based fMRI images. Here, T2prep BOLD fMRI was evaluated in an event-related paradigm for the first time. Functional experiments were performed using gradient-echo (GRE) EPI, spin-echo (SE) EPI, and T2prep BOLD fMRI during an event-related visual task in 10 healthy human subjects. Each fMRI method was performed with a low (3.4 × 3.4 × 4 mm3) and a high (1.5 mm isotropic) spatial resolution on 3T and a high resolution (1.5 mm isotropic) on 7T. Robust activation were detected in the visual cortex with all three fMRI methods. In each group of fMRI scans (3T low resolution, 3T high resolution, and 7T high resolution), GRE EPI showed the highest signal change (ΔS/S), largest full-width-at-half-maximum (FWHM) and longest time-to-peak (TTP) extracted from the hemodynamic response functions (HRF), indicating substantial signal contribution from large draining veins which have longer response times than microvessels. In contrast, T2prep BOLD showed the lowest ΔS/S, smallest FWHM, and shortest TTP, suggesting that T2prep BOLD may have a purer T2-weighted BOLD contrast that is more sensitive to microvessels compared to GRE/SE EPI BOLD. This trend was more obvious in fMRI scans performed with a lower spatial resolution on a lower field (3T with a 3.4 × 3.4 × 4 mm3 voxel). Scan-rescan reproducibility in the same subjects was comparable among the three fMRI methods. The results from the current study are expected to be useful to establish T2prep BOLD as a useful alternative fMRI approach for event-related fMRI in brain regions with large susceptibility artifacts.

Observing cognitive processes in time through functional MRI model comparison

The temporal specificity of functional magnetic resonance imaging (fMRI) is limited by a sluggish and locally variable hemodynamic response trailing the neural activity by seconds. Here, we demonstrate for an attention capture paradigm that it is, never the less, possible to extract information about the relative timing of regional brain activity during cognitive processes on the scale of 100 ms by comparing alternative signal models representing early versus late activation. We demonstrate that model selection is not driven by confounding regional differences in hemodynamic delay. We show, including replication, that the activity in the dorsal anterior insula is an early signal predictive of behavioral performance, while amygdala and ventral anterior insula signals are not. This specific finding provides new insights into how the brain assigns salience to stimuli and emphasizes the role of the dorsal anterior insula in this context. The general analytic approach, named "Cognitive Timing through Model Comparison" (CTMC), offers an exciting and novel method to identify functional brain subunits and their causal interactions.

Task-Based and Resting-State Functional MRI in Observing Eloquent Cerebral Areas Personalized for Epilepsy and Surgical Oncology Patients: A Review of the Current Evidence

Functional magnetic resonance imaging (fMRI) is among the newest techniques of advanced neuroimaging that offer the opportunity for neuroradiologists, neurophysiologists, neuro-oncologists, and neurosurgeons to pre-operatively plan and manage different types of brain lesions. Furthermore, it plays a fundamental role in the personalized evaluation of patients with brain tumors or patients with an epileptic focus for preoperative planning. While the implementation of task-based fMRI has increased in recent years, the existing resources and evidence related to this technique are limited. We have, therefore, conducted a comprehensive review of the available resources to compile a detailed resource for physicians who specialize in managing patients with brain tumors and seizure disorders. This review contributes to the existing literature because it highlights the lack of studies on fMRI and its precise role and applicability in observing eloquent cerebral areas in surgical oncology and epilepsy patients, which we believe is underreported. Taking these considerations into account would help to better understand the role of this advanced neuroimaging technique and, ultimately, improve patient life expectancy and quality of life.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, as differences can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

Characterization of Hemodynamic Alteration in Parkinson's Disease and Effect on Resting-State Connectivity

Functional magnetic resonance imaging (fMRI) is a convolution of latent neural activity and the hemodynamic response function (HRF). According to prior studies, the neurodegenerative process in idiopathic Parkinson's Disease (PD) interacts significantly with neuromuscular abnormalities. Although these underlying neuromuscular changes might influence the temporal characteristics of HRF and fMRI signals, relatively few studies have explored this possibility. We hypothesized that such alterations would engender changes in estimated functional connectivity (FC) in fMRI space compared to latent neural space. To test these theories, we calculated voxel-level HRFs by deconvolving resting-state fMRI data from PD patients (n = 61) and healthy controls (HC) (n = 47). Significant group differences in HRF (P < 0.05, Gaussian random field-corrected) were observed in several regions previously associated with PD. Subsequently, we focused on putamen-seed-based FC differences between the PD and HC groups using fMRI and latent neural signals. The results suggested that neglecting HRF variability may cultivate false-positive and false-negative FC group differences. Furthermore, HRF was related to dopamine receptor type 2 (DRD2) gene expression (P < 0.001, t = -7.06, false discover rate-corrected). Taken together, these findings reveal HRF variation and its possible underlying molecular mechanism in PD, and suggest that deconvolution could reduce the impact of HRF variation on FC group differences.

Changes in hemodynamic response function components reveal specific changes in neurovascular coupling in type 2 diabetes

Type 2 Diabetes Mellitus (T2DM) is a metabolic disease that leads to multiple vascular complications with concomitant changes in human neurophysiology, which may lead to long-term cognitive impairment, and dementia. Early impairments of neurovascular coupling can be studied using event-related functional magnetic resonance imaging (fMRI) designs. Here, we aimed to characterize the changes in the hemodynamic response function (HRF) in T2DM to probe components from the initial dip to late undershoot. We investigated whether the HRF morphology is altered throughout the brain in T2DM, by extracting several parameters of the fMRI response profiles in 141 participants (64 patients with T2DM and 77 healthy controls) performing a visual motion discrimination task. Overall, the patients revealed significantly different HRFs, which extended to all brain regions, suggesting that this is a general phenomenon. The HRF in T2DM was found to be more sluggish, with a higher peak latency and lower peak amplitude, relative slope to peak, and area under the curve. It also showed a pronounced initial dip, suggesting that the initial avidity for oxygen is not compensated for, and an absent or less prominent but longer undershoot. Most HRF parameters showed a higher dispersion and variability in T2DM. In sum, we provide a definite demonstration of an impaired hemodynamic response function in the early stages of T2DM, following a previous suggestion of impaired neurovascular coupling. The quantitative demonstration of a significantly altered HRF morphology in separate response phases suggests an alteration of distinct physiological mechanisms related to neurovascular coupling, which should be considered in the future to potentially halt the deterioration of the brain function in T2DM.

autohrf-an R package for generating data-informed event models for general linear modeling of task-based fMRI data

The analysis of task-related fMRI data at the level of individual participants is commonly based on general linear modeling (GLM), which allows us to estimate the extent to which the BOLD signal can be explained by the task response predictors specified in the event model. The predictors are constructed by convolving the hypothesized time course of neural activity with an assumed hemodynamic response function (HRF). However, our assumptions about the components of brain activity, including their onset and duration, may be incorrect. Their timing may also differ across brain regions or from person to person, leading to inappropriate or suboptimal models, poor fit of the model to actual data, and invalid estimates of brain activity. Here, we present an approach that uses theoretically driven models of task response to define constraints on which the final model is computationally derived using actual fMRI data. Specifically, we developed autohrf-an R package that enables the evaluation and data-driven estimation of event models for GLM analysis. The highlight of the package is the automated parameter search that uses genetic algorithms to find the onset and duration of task predictors that result in the highest fitness of GLM based on the fMRI signal under predefined constraints. We evaluated the usefulness of the autohrf package on two original datasets of task-related fMRI activity, a slow event-related spatial working memory study and a mixed state-item study using the flanker task, and on a simulated slow event-related working memory data. Our results suggest that autohrf can be used to efficiently construct and evaluate better task-related brain activity models to gain a deeper understanding of BOLD task response and improve the validity of model estimates. Our study also highlights the sensitivity of fMRI analysis with GLM to precise event model specification and the need for model evaluation, especially in complex and overlapping event designs.

Affective flexibility as a developmental building block of cognitive reappraisal: An fMRI study

Cognitive reappraisal is a form of emotion regulation that involves reinterpreting the meaning of a stimulus, often to downregulate one's negative affect. Reappraisal typically recruits distributed regions of prefrontal and parietal cortex to generate new appraisals and downregulate the emotional response in the amygdala. In the current study, we compared reappraisal ability in an fMRI task with affective flexibility in a sample of children and adolescents (ages 6-17, N = 76). Affective flexibility was defined as variability in valence interpretations of ambiguous (surprised) facial expressions from a second behavioral task. Results demonstrated that age and affective flexibility predicted reappraisal ability, with an interaction indicating that flexibility in children (but not adolescents) supports reappraisal success. Using a region of interest-based analysis of participants' BOLD time courses, we also found dissociable reappraisal-related brain mechanisms that support reappraisal success and affective flexibility. Specifically, late increases in middle prefrontal cortex activity supported reappraisal success and late decreases in amygdala activity supported flexibility. Together, these results suggest that our novel measure of affective flexibility - the ability to see multiple interpretations of an ambiguous emotional cue - may represent part of the developmental building blocks of cognitive reappraisal ability.

They Saw a Debate: Political Polarization Is Associated with Greater Multivariate Neural Synchrony When Viewing the Opposing Candidate Speak

With rampant polarization in current U.S. politics, it seems as though political partisans with opposing viewpoints are living in parallel realities. Indeed, prior research shows that people's impressions/attitudes toward political candidates are intertwined with their political affiliation. The current study investigated the relationship between political affiliation and intersubject neural synchrony of multivariate patterns of activity during naturalistic viewing of a presidential debate. Before the 2016 U.S. presidential election, 20 individuals varying in political affiliation underwent functional neuroimaging while watching the first debate between candidates Hillary Clinton and Donald Trump. Pairs of participants with more polarized political affiliations were higher in neural synchrony in a system of brain regions involved in self-referential processing when viewing the opposing candidate speak compared with that candidate's supporters regardless of which extreme of the political spectrum they occupied. Moreover, pairs of political partisans matching in the candidate they supported were higher in neural synchrony when watching the candidate they opposed compared with the one they both supported. These findings suggest that political groups' shared understanding may be driven more by perceptions of outgroups than of their own party/candidates.

Reconstruction of time-shifted hemodynamic response

Regression of voxel time course onto expected response is a standard procedure in functional magnetic resonance imaging that relies on exact onset time and shape of superimposed hemodynamic response functions. Elegant capture of time deviation by time derivative regressors appears complicated by shape distortion and limited to ±1 s, and is usually not exploited for reconstructing the true time-shifted response function together with its magnitude. This analysis of the time-derivative approach provides closed-form functional relations between time shift and regression coefficients that allow for hemodynamic shifts of ±5 s and can explain shape distortion and reconstruction behavior. Reliable absolute latencies were no smaller than 0.6 s in a best-case experiment. Confusions of latency are a previously undiscussed shortcoming where current limitation strategy may eliminate correct latencies and protect incorrect ones.

Spontaneous activity patterns in human motor cortex replay evoked activity patterns for hand movements

Spontaneous brain activity, measured with resting state fMRI (R-fMRI), is correlated among regions that are co-activated by behavioral tasks. It is unclear, however, whether spatial patterns of spontaneous activity within a cortical region correspond to spatial patterns of activity evoked by specific stimuli, actions, or mental states. The current study investigated the hypothesis that spontaneous activity in motor cortex represents motor patterns commonly occurring in daily life. To test this hypothesis 15 healthy participants were scanned while performing four different hand movements. Three movements (Grip, Extend, Pinch) were ecological involving grip and grasp hand movements; one control movement involving the rotation of the wrist was not ecological and infrequent (Shake). They were also scanned at rest before and after the execution of the motor tasks (resting-state scans). Using the task data, we identified movement-specific patterns in the primary motor cortex. These task-defined patterns were compared to resting-state patterns in the same motor region. We also performed a control analysis within the primary visual cortex. We found that spontaneous activity patterns in the primary motor cortex were more like task patterns for ecological than control movements. In contrast, there was no difference between ecological and control hand movements in the primary visual area. These findings provide evidence that spontaneous activity in human motor cortex forms fine-scale, patterned representations associated with behaviors that frequently occur in daily life.

Analysis and visualization methods for detecting functional activation using laser speckle contrast imaging

BACKGROUND

Previous studies have used regional cerebral blood flow (CBF) hemodynamic response to measure brain activities. In this work, we use a laser speckle contrast imaging (LSCI) apparatus to sample the CBF activation in somatosensory cortex (S1BF) with repetitive whisker stimulation. Traditionally, the CBF activations were processed by depicting the change percentage above baseline; however, it is not clear how different methods influence the detection of activations.

AIMS

Thus, in this work we investigate the influence of different methods to detect activations in LSCI.

MATERIALS & METHODS

First, principal component analysis (PCA) was performed to denoise the CBF signal. As the signal of the first principal component (PC1) showed the highest correlation with the S1BF CBF response curve, PC1 was used in the subsequent analyses. Then, we used fast Fourier transform (FFT) to evaluate the frequency properties of the LSCI images and the activation map was generated based on the amplitude of the central frequency. Furthermore, Pearson's correlation coefficient (C-C) analysis and a general linear model (GLM) were performed to estimate the S1BF activation based on the time series of PC1.

RESULTS

We found that GLM performed better in identifying activation than C-C. Additionally, the activation maps generated by FFT were similar to those obtained by GLM. Particularly, the superficial vein and arterial vessels separated the activation region as segmented activated areas, and the regions with unresolved vessels showed a common activation for whisker stimulation.

DISCUSSION AND CONCLUSION

Our research analyzed the extent to which PCA can extract meaningful information from the signal and we compared the performance for detecting brain functional activation between different methods that rely on LSCI. This can be used as a reference for LSCI researchers on choosing the best method to estimate brain activation.

Revisiting the cerebral hemodynamics of awake, freely moving rats with repeated ketamine self-administration using a miniature photoacoustic imaging system

SIGNIFICANCE

Revealing the dynamic associations between brain functions and behaviors is a significant challenge in neurotechnology, especially for awake subjects. Imaging cerebral hemodynamics in awake animal models is important because the collected data more realistically reflect human disease states.

AIM

We previously reported a miniature head-mounted scanning photoacoustic imaging (hmPAI) system. In the present study, we utilized this system to investigate the effects of ketamine on the cerebral hemodynamics of normal rats and rats subjected to prolonged ketamine self-administration.

APPROACH

The cortical superior sagittal sinus (SSS) was continuously monitored. The full-width at half-maximum (FWHM) of the photoacoustic (PA) A-line signal was used as an indicator of the SSS diameter, and the number of pixels in PA B-scan images was used to investigate changes in the cerebral blood volume (CBV).

RESULTS

We observed a significantly higher FWHM (blood vessel diameter) and CBV in normal rats injected with ketamine than in normal rats injected with saline. For rats subjected to prolonged ketamine self-administration, no significant changes in either the blood vessel diameter or CBV were observed.

CONCLUSIONS

The lack of significant change in prolonged ketamine-exposed rats was potentially due to an increased ketamine tolerance. Our device can reliably detect changes in the dilation of cortical blood vessels and the CBV. This study validates the utility of the developed hmPAI system in an awake, freely moving rat model for behavioral, cognitive, and preclinical cerebral disease studies.

A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state

Awakening from sleep reflects a profound transformation in neural activity and behavior. The thalamus is a key controller of arousal state, but whether its diverse nuclei exhibit coordinated or distinct activity at transitions in behavioral arousal state is unknown. Using fast fMRI at ultra-high field (7 Tesla), we measured sub-second activity across thalamocortical networks and within nine thalamic nuclei to delineate these dynamics during spontaneous transitions in behavioral arousal state. We discovered a stereotyped sequence of activity across thalamic nuclei and cingulate cortex that preceded behavioral arousal after a period of inactivity, followed by widespread deactivation. These thalamic dynamics were linked to whether participants subsequently fell back into unresponsiveness, with unified thalamic activation reflecting maintenance of behavior. These results provide an outline of the complex interactions across thalamocortical circuits that orchestrate behavioral arousal state transitions, and additionally, demonstrate that fast fMRI can resolve sub-second subcortical dynamics in the human brain.

Temporal stability of the hemodynamic response function across the majority of human cerebral cortex

The hemodynamic response function (HRF) measured with functional magnetic resonance imaging is generated by vascular and metabolic responses evoked by brief (<4 s) stimuli. It is known that the human HRF varies across cortex, between subjects, with stimulus paradigms, and even between different measurements in the same cortical location. However, our results demonstrate that strong HRFs are remarkably repeatable across sessions separated by time intervals up to 3 months. In this study, a multisensory stimulus was used to activate and measure the HRF across the majority of cortex (>70%, with lesser reliability observed in some areas of prefrontal cortex). HRFs were measured with high spatial resolution (2‐mm voxels) in central gray matter to minimize variations caused by partial‐volume effects. HRF amplitudes and temporal dynamics were highly repeatable across four sessions in 20 subjects. Positive and negative HRFs were consistently observed across sessions and subjects. Negative HRFs were generally weaker and, thus, more variable than positive HRFs. Statistical measurements showed that across‐session variability is highly correlated to the variability across events within a session; these measurements also indicated a normal distribution of variability across cortex. The overall repeatability of the HRFs over long time scales generally supports the long‐term use of event‐related functional magnetic resonance imaging protocols.

Reappraisal-related downregulation of amygdala BOLD activation occurs only during the late trial window

During cognitive reappraisal, an individual reinterprets the meaning of an emotional stimulus to regulate the intensity of their emotional response. Prefrontal cortex activity has been found to support reappraisal and is putatively thought to downregulate the amygdala response to these stimuli. The timing of these regulation-related responses during the course of a trial, however, remains poorly understood. In the current fMRI study, participants were instructed to view or reappraise negative images and then rate how negative they felt following each image. The hemodynamic response function was estimated in 11 regions of interest for the entire time course of the trial including image viewing and rating. Notably, within the amygdala there was no evidence of downregulation in the early (picture viewing) window of the trial, only in the late (rating) window, which also correlated with a behavioral measure of reappraisal success. With respect to the prefrontal regions, some (e.g., inferior frontal gyrus) showed reappraisal-related activation in the early window, whereas others (e.g., middle frontal gyrus) showed increased activation primarily in the late window. These results highlight the temporal dynamics of different brain regions during emotion regulation and suggest that the amygdala response to negative images need not be immediately dampened to achieve successful cognitive reappraisal.

The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study

Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research.

Action goals and the praxis network: an fMRI study

The praxis representation network (PRN) of the left cerebral hemisphere is typically linked to the control of functional interactions with familiar tools. Surprisingly, little is known about the PRN engagement in planning and execution of tool-directed actions motivated by non-functional but purposeful action goals. Here we used functional neuroimaging to perform both univariate and multi-voxel pattern analyses (MVPA) in 20 right-handed participants who planned and later executed, with their dominant and non-dominant hands, disparate grasps of tools for different goals, including: (1) planning simple vs. demanding functional grasps of conveniently vs. inconveniently oriented tools with an intention to immediately use them, (2) planning simple—but non-functional—grasps of inconveniently oriented tools with a goal to pass them to a different person, (3) planning reaching movements directed at such tools with an intention to move/push them with the back of the hand, and (4) pantomimed execution of the earlier planned tasks. While PRN contributed to the studied interactions with tools, the engagement of its critical nodes, and/or complementary right hemisphere processing, was differently modulated by task type. E.g., planning non-functional/structural grasp-to-pass movements of inconveniently oriented tools, regardless of the hand, invoked the left parietal and prefrontal nodes significantly more than simple, non-demanding functional grasps. MVPA corroborated decoding capabilities of critical PRN areas and some of their right hemisphere counterparts. Our findings shed new lights on how performance of disparate action goals influences the extraction of object affordances, and how or to what extent it modulates the neural activity within the parieto-frontal brain networks.

Auditory Target Detection Enhances Visual Processing and Hippocampal Functional Connectivity

Though dividing one's attention between two input streams typically impairs performance, detecting a behaviorally relevant stimulus can sometimes enhance the encoding of unrelated information presented at the same time. Previous research has shown that selection of this kind boosts visual cortical activity and memory for concurrent items. An important unanswered question is whether such effects are reflected in processing quality and functional connectivity in visual regions and in the hippocampus. In this fMRI study, participants were asked to memorize a stream of naturalistic images and press a button only when they heard a predefined target tone (400 or 1,200 Hz, counterbalanced). Images could be presented with a target tone, with a distractor tone, or without a tone. Auditory target detection increased activity throughout the ventral visual cortex but lowered it in the hippocampus. Enhancements in functional connectivity between the ventral visual cortex and the hippocampus were also observed following auditory targets. Multi-voxel pattern classification of image category was more accurate on target tone trials than on distractor and no tone trials in the fusiform gyrus and parahippocampal gyrus. This effect was stronger in visual cortical clusters whose activity was more correlated with the hippocampus on target tone than on distractor tone trials. In agreement with accounts suggesting that subcortical noradrenergic influences play a role in the attentional boost effect, auditory target detection also caused an increase in locus coeruleus activity and phasic pupil responses. These findings outline a network of cortical and subcortical regions that are involved in the selection and processing of information presented at behaviorally relevant moments.

Altered linear coupling between stimulus-evoked blood flow and oxygen metabolism in the aging human brain

Neural-vascular coupling (NVC) is the process by which oxygen and nutrients are delivered to metabolically active neurons by blood vessels. Murine models of NVC disruption have revealed its critical role in healthy neural function. We hypothesized that, in humans, aging exerts detrimental effects upon the integrity of the neural-glial-vascular system that underlies NVC. To test this hypothesis, calibrated functional magnetic resonance imaging (cfMRI) was used to characterize age-related changes in cerebral blood flow (CBF) and oxygen metabolism during visual cortex stimulation. Thirty-three younger and 27 older participants underwent cfMRI scanning during both an attention-controlled visual stimulation task and a hypercapnia paradigm used to calibrate the blood-oxygen-level-dependent signal. Measurement of stimulus-evoked blood flow and oxygen metabolism permitted calculation of the NVC ratio to assess the integrity of neural-vascular communication. Consistent with our hypothesis, we observed monotonic NVC ratio increases with increasing visual stimulation frequency in younger adults but not in older adults. Age-related changes in stimulus-evoked cerebrovascular and neurometabolic signal could not fully explain this disruption; increases in stimulus-evoked neurometabolic activity elicited corresponding increases in stimulus-evoked CBF in younger but not in older adults. These results implicate age-related, demand-dependent failures of the neural-glial-vascular structures that comprise the NVC system.

Weak Task Synchronization of Default Mode Network in Task Based Paradigms

The brains Default mode network (DMN) is generally characterized by brain areas that gets deactivated upon the presentation of a wide variety of externally focused, attention demanding tasks. These areas also exhibit significant intra-DMN functional connectivity and significant negative functional connectivity with other brain areas, especially with attention networks, in both resting state and task conditions. Therefore, the DMN has been hypothesized to be involved in internally directed cognitive activities such as autobiographical recall of the past, future planning and mind wandering. Recent research has discovered the role of bottom-up attention in modulating the behaviour of DMN. We hypothesize that the de-engagement of the DMN regions upon the presentation of an externally-focused attention-demanding stimulus may not be strictly stimulus locked and may exhibit significant trial-to-trial as well as subject-to-subject variability. Due to the involvement of frontoparietal control network in modulating the anticorrelations between DMN and dorsal attention network (DAN), we expect the DMN regions to have lower inter-trial and inter-subject synchronization in their fMRI BOLD responses as compared to the bottom-up early-sensory task-positive regions. To test this hypothesis, we designed new statistical methods called Inter Trial Temporal Synchronization Analysis (IT-TSA) and Inter Subject TSA (IS-TSA) to analyse variability across trials and subjects respectively. We analysed four publicly available datasets (total 223 subjects) across seven tasks related to different cognitive modalities and found out that there is significantly low stimulus-locked synchronization across trials and subjects in the DMN regions as compared to early sensory task positive regions. Our study challenges the understanding of DMN as a strictly task-negative region and supports the recent findings that DMN acts as an active component associated with intrinsic processing which deactivates differentially and non-linearly across trials and subjects in the presence of extrinsic processes.

Algebra dissociates from arithmetic in the brain semantic network

Background

Mathematical expressions mainly include arithmetic (such as 8 − (1 + 3)) and algebra (such as a − (b + c)). Previous studies have shown that both algebraic processing and arithmetic involved the bilateral parietal brain regions. Although previous studies have revealed that algebra was dissociated from arithmetic, the neural bases of the dissociation between algebraic processing and arithmetic is still unclear. The present study uses functional magnetic resonance imaging (fMRI) to identify the specific brain networks for algebraic and arithmetic processing.

Methods

Using fMRI, this study scanned 30 undergraduates and directly compared the brain activation during algebra and arithmetic. Brain activations, single-trial (item-wise) interindividual correlation and mean-trial interindividual correlation related to algebra processing were compared with those related to arithmetic. The functional connectivity was analyzed by a seed-based region of interest (ROI)-to-ROI analysis.

Results

Brain activation analyses showed that algebra elicited greater activation in the angular gyrus and arithmetic elicited greater activation in the bilateral supplementary motor area, left insula, and left inferior parietal lobule. Interindividual single-trial brain-behavior correlation revealed significant brain-behavior correlations in the semantic network, including the middle temporal gyri, inferior frontal gyri, dorsomedial prefrontal cortices, and left angular gyrus, for algebra. For arithmetic, the significant brain-behavior correlations were located in the phonological network, including the precentral gyrus and supplementary motor area, and in the visuospatial network, including the bilateral superior parietal lobules. For algebra, significant positive functional connectivity was observed between the visuospatial network and semantic network, whereas for arithmetic, significant positive functional connectivity was observed only between the visuospatial network and phonological network.

Conclusion

These findings suggest that algebra relies on the semantic network and conversely, arithmetic relies on the phonological and visuospatial networks.

Neural Activity Across the Dorsolateral Prefrontal Cortex and Risk for Suicidal Ideation and Self-Injury

The current study investigated the neural correlates of non-suicidal self-injury (NSSI) and suicidal ideation across the dorsolateral prefrontal cortex. Two-hundred ninety-six undergraduates solved anagram tasks while being monitored by a functional near-infrared spectroscopy device, and completed a questionnaire assessing behaviors and symptoms. Repeated measures analysis of variance revealed location-specific changes in neural activity based on NSSI, suicidal ideation, task type, and cognitive demand. The presence of suicidal ideation was associated with specific patterns of neural activity, modified by sex and task type. Interestingly, participants who engaged in NSSI exhibited some deactivation of the dlPFC when faced with more difficult cognitive challenges. Future research on these processes may allow for noninvasive imaging techniques to help screen risk for suicidality and NSSI.

Disentangling craving- and valence-related brain responses to smoking cues in individuals with nicotine use disorder

Tobacco smoking is one of the leading causes of preventable death and disease worldwide. Most smokers want to quit, but relapse rates are high. To improve current smoking cessation treatments, a better understanding of the underlying mechanisms of nicotine dependence and related craving behaviour is needed. Studies on cue‐driven cigarette craving have been a particularly useful tool for investigating the neural mechanisms of drug craving. Here, functional neuroimaging studies in humans have identified a core network of craving‐related brain responses to smoking cues that comprises of amygdala, anterior cingulate cortex, orbitofrontal cortex, posterior cingulate cortex and ventral striatum. However, most functional Magnetic Resonance Imaging (fMRI) cue‐reactivity studies do not adjust their stimuli for emotional valence, a factor assumed to confound craving‐related brain responses to smoking cues. Here, we investigated the influence of emotional valence on key addiction brain areas by disentangling craving‐ and valence‐related brain responses with parametric modulators in 32 smokers. For one of the suggested key regions for addiction, the amygdala, we observed significantly stronger brain responses to the valence aspect of the presented images than to the craving aspect. Our results emphasize the need for carefully selecting stimulus material for cue‐reactivity paradigms, in particular with respect to emotional valence. Further, they can help designing future research on teasing apart the diverse psychological dimensions that comprise nicotine dependence and, therefore, can lead to a more precise mapping of craving‐associated brain areas, an important step towards more tailored smoking cessation treatments.

rsHRF: A toolbox for resting-state HRF estimation and deconvolution

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.

Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox

An important goal in neuroscience is to elucidate the causal relationships between the brain's different regions. This can help reveal the brain's functional circuitry and diagnose lesions. Currently there are a lack of approaches to functional connectome estimation that leverage the state-of-the-art in deep learning architectures and training methodologies. Therefore, we propose a new framework based on a vector auto-regressive deep neural network (VARDNN) architecture. Our approach consists of a set of nodes, each with a deep neural network structure. These nodes can be mapped to any spatial sub-division based on the data to be analyzed, such as anatomical brain regions from which representative neural signals can be obtained. VARDNN learns to reproduce experimental time series data using modern deep learning training techniques. Based on this, we developed two novel directed functional connectivity (dFC) measures, namely VARDNN-DI and VARDNN-GC. We evaluated our measures against a number of existing functional connectome estimation measures, such as partial correlation and multivariate Granger causality combined with large dimensionality counter-measure techniques. Our measures outperformed them across various types of ground truth data, especially as the number of nodes increased. We applied VARDNN to fMRI data to compare the dFC between 41 healthy control vs. 32 Alzheimer's disease subjects. Our VARDNN-DI measure detected lesioned regions consistent with previous studies and separated the two groups well in a subject-wise evaluation framework. Summarily, the VARDNN framework has powerful capabilities for whole brain dFC estimation. We have implemented VARDNN as an open-source toolbox that can be freely downloaded for researchers who wish to carry out functional connectome analysis on their own data.

A practical guide for synthetic fNIRS data generation

The use of a large and diversified ground-truth synthetic fNIRS dataset enables researchers to objectively validate and compare data analysis procedures. In this work, we describe each step of the synthetic data generation workflow and we provide tools to generate the dataset.