Causal interactions in brain networks predict pain levels in trigeminal neuralgia.

Trigeminal neuralgia (TN) is a highly debilitating facial pain condition. Magnetic resonance imaging (MRI) is the main method for generating insights into the central mechanisms of TN pain in humans. Studies have found both structural and functional abnormalities in various brain structures in TN patients as compared with healthy controls. Whereas studies have also examined aberrations in brain networks in TN, no studies have to date investigated causal interactions in these brain networks and related these causal interactions to the levels of TN pain. We recorded fMRI data from 39 TN patients who either rested comfortably in the scanner during the resting state session or tracked their pain levels during the pain tracking session. Applying Granger causality to analyze the data and requiring consistent findings across the two scanning sessions, we found 5 causal interactions, including: (1) Thalamus → dACC, (2) Caudate → Inferior temporal gyrus, (3) Precentral gyrus → Inferior temporal gyrus, (4) Supramarginal gyrus → Inferior temporal gyrus, and (5) Bankssts → Inferior temporal gyrus, that were consistently associated with the levels of pain experienced by the patients. Utilizing these 5 causal interactions as predictor variables and the pain score as the predicted variable in a linear multiple regression model, we found that in both pain tracking and resting state sessions, the model was able to explain ∼36 % of the variance in pain levels, and importantly, the model trained on the 5 causal interaction values from one session was able to predict pain levels using the 5 causal interaction values from the other session, thereby cross-validating the models. These results, obtained by applying novel analytical methods to neuroimaging data, provide important insights into the pathophysiology of TN and could inform future studies aimed at developing innovative therapies for treating TN.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

Decoding fMRI data with support vector machines and deep neural networks.

BACKGROUND: Multivoxel pattern analysis (MVPA) examines fMRI activation patterns associated with different cognitive conditions. Support vector machines (SVMs) are the predominant method in MVPA. While SVM is intuitive and easy to apply, it is mainly suitable for analyzing data that are linearly separable. Convolutional neural networks (CNNs) are known to have the ability to approximate nonlinear relationships. Applications of CNN to fMRI data are beginning to appear with increasing frequency, but our understanding of the similarities and differences between CNN models and SVM models is limited. NEW METHOD: We compared the two methods when they are applied to the same datasets. Two datasets were considered: (1) fMRI data collected from participants during a cued visual spatial attention task and (2) fMRI data collected from participants viewing natural images containing varying degrees of affective content. RESULTS: We found that (1) both SVM and CNN are able to achieve above-chance decoding accuracies for attention control and emotion processing in both the primary visual cortex and the whole brain, (2) the CNN decoding accuracies are consistently higher than that of the SVM, (3) the SVM and CNN decoding accuracies are generally not correlated, and (4) the heatmaps derived from SVM and CNN are not significantly overlapping. COMPARISON WITH EXISTING METHODS: By comparing SVM and CNN we pointed out the similarities and differences between the two methods. CONCLUSIONS: SVM and CNN rely on different neural features for classification. Applying both to the same data may yield a more comprehensive understanding of neuroimaging data.

Top-Down Biasing of Visual Cortical Activity Encodes Attended Information and Facilitates Behavioral Performance in Visual Spatial Attention.

Top-down attention plays a vital role in selecting relevant stimuli and suppressing distracting information. During top-down visual-spatial attention, control signals from the dorsal attention network modulate the baseline neuronal activity in the visual cortex in favor of task-relevant stimuli. While several studies have demonstrated that baseline shift during anticipatory attention occurs in multiple visual areas, such effects have not been systematically investigated across the visual hierarchy, especially when different attention conditions are matched for stimulus and task factors. In this fMRI study, we investigated anticipatory attention signals using univariate and multivariate (MVPA) analysis in multiple visual cortical areas. First, the univariate analysis yielded significant activation differences in higher-order visual areas, with the effect weaker in early visual areas. Second, however, in contrast, MVPA decoding was significant in predicting attention conditions in all visual areas and IPS, with lower-order visual areas (e.g., V1) having greater decoding accuracy than higher-order visual areas (e.g., LO1). Third, the strength of decoding accuracy predicted the behavioral performance in the discrimination task. All the results were highly replicable and consistent across two datasets with same experimental paradigms but recorded at two research sites, and two experimental conditions where the direction of spatial attention was driven either by external instructions (cue-instructed attention) or from internal decisions (free-choice attention). Our results provide clear evidence, not available in past univariate investigations, that top-down attentional control signals selectively bias neuronal processing throughout the visual hierarchy, and that this biasing is correlated with the task performance.

Optimizing cognitive neuroscience experiments for separating event- related fMRI BOLD responses in non-randomized alternating designs.

Functional magnetic resonance imaging (fMRI) has revolutionized human brain research. But there exists a fundamental mismatch between the rapid time course of neural events and the sluggish nature of the fMRI blood oxygen level-dependent (BOLD) signal, which presents special challenges for cognitive neuroscience research. This limitation in the temporal resolution of fMRI puts constraints on the information about brain function that can be obtained with fMRI and also presents methodological challenges. Most notably, when using fMRI to measure neural events occurring closely in time, the BOLD signals may temporally overlap one another. This overlap problem may be exacerbated in complex experimental paradigms (stimuli and tasks) that are designed to manipulate and isolate specific cognitive-neural processes involved in perception, cognition, and action. Optimization strategies to deconvolve overlapping BOLD signals have proven effective in providing separate estimates of BOLD signals from temporally overlapping brain activity, but there remains reduced efficacy of such approaches in many cases. For example, when stimulus events necessarily follow a non-random order, like in trial-by-trial cued attention or working memory paradigms. Our goal is to provide guidance to improve the efficiency with which the underlying responses evoked by one event type can be detected, estimated, and distinguished from other events in designs common in cognitive neuroscience research. We pursue this goal using simulations that model the nonlinear and transient properties of fMRI signals, and which use more realistic models of noise. Our simulations manipulated: (i) Inter-Stimulus-Interval (ISI), (ii) proportion of so-called null events, and (iii) nonlinearities in the BOLD signal due to both cognitive and design parameters. We offer a theoretical framework along with a python toolbox called deconvolve to provide guidance on the optimal design parameters that will be of particular utility when using non-random, alternating event sequences in experimental designs. In addition, though, we also highlight the challenges and limitations in simultaneously optimizing both detection and estimation efficiency of BOLD signals in these common, but complex, cognitive neuroscience designs.

Transcranial stimulation of alpha oscillations modulates brain state dynamics in sustained attention.

The brain operates an advanced complex system to support mental activities. Cognition is thought to emerge from dynamic states of the complex brain system, which are organized spatially through large-scale neural networks and temporally via neural synchrony. However, specific mechanisms underlying these processes remain obscure. Applying high-definition alpha-frequency transcranial alternating-current stimulation (HD α-tACS) in a continuous performance task (CPT) during functional resonance imaging (fMRI), we causally elucidate these major organizational architectures in a key cognitive operation-sustained attention. We demonstrated that α-tACS enhanced both electroencephalogram (EEG) alpha power and sustained attention, in a correlated fashion. Akin to temporal fluctuations inherent in sustained attention, our hidden Markov modeling (HMM) of fMRI timeseries uncovered several recurrent, dynamic brain states, which were organized through a few major neural networks and regulated by the alpha oscillation. Specifically, during sustain attention, α-tACS regulated the temporal dynamics of the brain states by suppressing a Task-Negative state (characterized by activation of the default mode network/DMN) and Distraction state (with activation of the ventral attention and visual networks). These findings thus linked dynamic states of major neural networks and alpha oscillations, providing important insights into systems-level mechanisms of attention. They also highlight the efficacy of non-invasive oscillatory neuromodulation in probing the functioning of the complex brain system and encourage future clinical applications to improve neural systems health and cognitive performance.

Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks.

Multivoxel pattern analysis (MVPA) examines the differences in fMRI activation patterns associated with different cognitive conditions and provides information not possible with the conventional univariate analysis. Support vector machines (SVMs) are the predominant machine learning method in MVPA. SVMs are intuitive and easy to apply. The limitation is that it is a linear method and mainly suitable for analyzing data that are linearly separable. Convolutional neural networks (CNNs), a class of AI models originally developed for object recognition, are known to have the ability to approximate nonlinear relationships. CNNs are rapidly becoming an alternative to SVMs. The purpose of this study is to compare the two methods when they are applied to the same datasets. Two datasets were considered: (1) fMRI data collected from participants during a cued visual spatial attention task (the attention dataset) and (2) fMRI data collected from participants viewing natural images containing varying degrees of affective content (the emotion dataset). We found that (1) both SVM and CNN are able to achieve above chance level decoding accuracies for attention control and emotion processing in both the primary visual cortex and the whole brain with, (2) the CNN decoding accuracies are consistently higher than that of the SVM, (3) the SVM and CNN decoding accuracies are generally not correlated with each other, and (4) the heatmaps derived from SVM and CNN are not significantly overlapping. These results suggest that (1) there are both linearly separable features and nonlinearly separable features in fMRI data that distinguish cognitive conditions and (2) applying both SVM and CNN to the same data may yield a more comprehensive understanding of neuroimaging data. Key points: We compared the performance and characteristics of SVM and CNN, two major methods in MVPA analysis of neuroimaging data, by applying them to the same two fMRI datasets.Both SVM and CNN achieved decoding accuracies above chance level for both datasets in the chosen ROIs and the CNN decoding accuracies were consistently higher than those of SVM.The heatmaps derived from SVM and CNN, which assess the contribution of voxels or brain regions to MVPA decoding performance, showed no significant overlap, providing evidence that the two methods depend on distinct brain activity patterns for decoding cognitive conditions.

Dual-functional Network Regulation Underlies the Central Executive System in Working Memory.

The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON â†’ FPN, CON â†’ DMN, CON â†’ visual areas, FPN â†’ visual areas, and phonological areas â†’ FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON â†’ FPN, CON â†’ DMN, visual areas â†’ CON, and visual areas â†’ FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.

Imaging the neural substrate of trigeminal neuralgia pain using deep learning.

Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were 6 regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex (dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions are taken to represent signature centers of TN pain and provide target areas for future studies seeking to understand the central mechanisms of TN.

Time Courses of Attended and Ignored Object Representations.

Selective attention prioritizes information that is relevant to behavioral goals. Previous studies have shown that attended visual information is processed and represented more efficiently, but distracting visual information is not fully suppressed, and may also continue to be represented in the brain. In natural vision, to-be-attended and to-be-ignored objects may be present simultaneously in the scene. Understanding precisely how each is represented in the visual system, and how these neural representations evolve over time, remains a key goal in cognitive neuroscience. In this study, we recorded EEG while participants performed a cued object-based attention task that involved attending to target objects and ignoring simultaneously presented and spatially overlapping distractor objects. We performed support vector machine classification on the stimulus-evoked EEG data to separately track the temporal dynamics of target and distractor representations. We found that (1) both target and distractor objects were decodable during the early phase of object processing (∼100 msec to ∼200 msec after target onset), and (2) the representations of both objects were sustained over time, remaining decodable above chance until ∼1000-msec latency. However, (3) the distractor object information faded significantly beginning after about 300-msec latency. These findings provide information about the fate of attended and ignored visual information in complex scene perception.

Top-down control of the left visual field bias in cued visual spatial attention.

A left visual field (LVF) bias in perceptual judgments, response speed, and discrimination accuracy has been reported in humans. Cognitive factors, such as visual spatial attention, are known to modulate or even eliminate this bias. We investigated this problem by recording pupillometry together with functional magnetic resonance imaging (fMRI) in a cued visual spatial attention task. We observed that (i) the pupil was significantly more dilated following attend-right than attend-left cues, (ii) the task performance (e.g. reaction time [RT]) did not differ between attend-left and attend-right trials, and (iii) the difference in cue-related pupil dilation between attend-left and attend-right trials was inversely related to the corresponding difference in RT. Neuroscientically, correlating the difference in cue-related pupil dilation with the corresponding cue-related fMRI difference yielded activations primarily in the right hemisphere, including the right intraparietal sulcus and the right ventrolateral prefrontal cortex. These results suggest that (i) there is an asymmetry in visual spatial attention control, with the rightward attention control being more effortful than the leftward attention control, (ii) this asymmetry underlies the reduction or the elimination of the LVF bias, and (iii) the components of the attentional control networks in the right hemisphere are likely part of the neural substrate of the observed asymmetry in attentional control.

Aftereffects of frontoparietal theta tACS on verbal working memory: Behavioral and neurophysiological analysis.

Verbal working memory is supported by a left-lateralized frontoparietal theta oscillatory (4-8 Hz) network. We tested whether stimulating the left frontoparietal network at theta frequency during verbal working memory can produce observable after-stimulation effects in behavior and neurophysiology. Weak theta-band alternating electric currents were delivered via two 4 × 1 HD electrode arrays centered at F3 and P3. Three stimulation configurations, including in-phase, anti-phase, or sham, were tested on three different days in a cross-over (within-subject) design. On each test day, the subject underwent three experimental sessions: pre-, during- and post-stimulation sessions. In all sessions, the subject performed a Sternberg verbal working memory task with three levels of memory load (load 2, 4 and 6), imposing three levels of cognitive demand. Analyzing behavioral and EEG data from the post-stimulation session, we report two main observations. First, in-phase stimulation improved task performance in subjects with higher working memory capacity (WMC) under higher memory load (load 6). Second, in-phase stimulation enhanced frontoparietal theta synchrony during working memory retention in subjects with higher WMC under higher memory loads (load 4 and load 6), and the enhanced frontoparietal theta synchronization is mainly driven by enhanced frontal→parietal theta Granger causality. These observations suggest that (1) in-phase theta transcranial alternating current stimulation (tACS) during verbal working memory can result in observable behavioral and neurophysiological consequences post stimulation, (2) the short-term plasticity effects are state- and individual-dependent, and (3) enhanced executive control underlies improved behavioral performance.

Free Water Fraction Predicts Cognitive Decline for Individuals with Idiopathic Parkinson's disease.

INTRODUCTION: Free water fraction (FWF) is considered a metric of microstructural integrity and may be useful in predicting cognitive decline in idiopathic Parkinson's Disease (PD). We sought to determine if higher FWF within the dorsal portion of the caudate nucleus and basal nucleus of Meynert, two regions associated with cognitive decline in PD, predict change in cognition over a two-year span. Due to the existence of cognitive and neurophysiological subgroups within PD, we statistically categorized participants based on FWF in these regions. METHODS: At baseline, participants completed a research cognitive protocol followed by MRI structural and diffusion metrics. We used k-means cluster analysis with average FWF values from bilateral basal nucleus of Meynert and dorsal caudate to create data-driven FWF clusters for baseline. Two-year reliable change indices were calculated for metrics of language, visuospatial, memory, cognitive flexibility, and reasoning domains. Reliable change scores were compared between the clusters and non-PD peers. RESULTS: Baseline participants included 174 participants (112 PD, 62 non-PD). Cluster analysis yielded three clusters: low FWF in both regions of interest (ROIs), high FWF in both ROIs, and moderate FWF in both ROIs. Reliable change analyses were completed on 93 participants (67 PD, 26 non-PD). After controlling for age and education, the High FWF cluster declined more than non-PD peers in every domain except memory. CONCLUSION: Individuals with high FWF in regions associated with cognitive decline in PD show significant decline across several cognitive domains compared to non-PD peers. Future research should include FWF in additional cortical regions.

Anticipatory attention is a stable state induced by transient control mechanisms.

Anticipatory attention is a neurocognitive state in which attention control regions bias neural activity in sensory cortical areas to facilitate the selective processing of incoming targets. Previous electroencephalographic (EEG) studies have identified event-related potential (ERP) signatures of anticipatory attention, and implicated alpha band (8-12 Hz) EEG oscillatory activity in the selective control of neural excitability in visual cortex. However, the degree to which ERP and alpha band measures reflect related or distinct underlying neural processes remains to be further understood. To investigate this question, we analyzed EEG data from 20 human participants performing a cued object-based attention task. We used support vector machine (SVM) decoding analysis to compare the attentional time courses of ERP signals and alpha band power. We found that ERP signals encoding attentional instructions are dynamic and precede stable attention-related changes in alpha power, suggesting that ERP and alpha power reflect distinct neural processes. We proposed that the ERP patterns reflect transient attentional orienting signals originating in higher order control areas, whereas the patterns of synchronized oscillatory neural activity in the alpha band reflect a sustained attentional state. These findings support the hypothesis that anticipatory attention involves transient top-down control signals that establish more stable neural states in visual cortex, enabling selective sensory processing.

Decoding the temporal dynamics of affective scene processing.

Natural images containing affective scenes are used extensively to investigate the neural mechanisms of visual emotion processing. Functional fMRI studies have shown that these images activate a large-scale distributed brain network that encompasses areas in visual, temporal, and frontal cortices. The underlying spatial and temporal dynamics, however, remain to be better characterized. We recorded simultaneous EEG-fMRI data while participants passively viewed affective images from the International Affective Picture System (IAPS). Applying multivariate pattern analysis to decode EEG data, and representational similarity analysis to fuse EEG data with simultaneously recorded fMRI data, we found that: (1) ∼80 ms after picture onset, perceptual processing of complex visual scenes began in early visual cortex, proceeding to ventral visual cortex at ∼100 ms, (2) between ∼200 and ∼300 ms (pleasant pictures: ∼200 ms; unpleasant pictures: ∼260 ms), affect-specific neural representations began to form, supported mainly by areas in occipital and temporal cortices, and (3) affect-specific neural representations were stable, lasting up to ∼2 s, and exhibited temporally generalizable activity patterns. These results suggest that affective scene representations in the brain are formed temporally in a valence-dependent manner and may be sustained by recurrent neural interactions among distributed brain areas.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series.

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

Transcranial stimulation of alpha oscillations up-regulates the default mode network.

The default mode network (DMN) is the most-prominent intrinsic connectivity network, serving as a key architecture of the brain's functional organization. Conversely, dysregulated DMN is characteristic of major neuropsychiatric disorders. However, the field still lacks mechanistic insights into the regulation of the DMN and effective interventions for DMN dysregulation. The current study approached this problem by manipulating neural synchrony, particularly alpha (8 to 12 Hz) oscillations, a dominant intrinsic oscillatory activity that has been increasingly associated with the DMN in both function and physiology. Using high-definition alpha-frequency transcranial alternating current stimulation (α-tACS) to stimulate the cortical source of alpha oscillations, in combination with simultaneous electroencephalography and functional MRI (EEG-fMRI), we demonstrated that α-tACS (versus Sham control) not only augmented EEG alpha oscillations but also strengthened fMRI and (source-level) alpha connectivity within the core of the DMN. Importantly, increase in alpha oscillations mediated the DMN connectivity enhancement. These findings thus identify a mechanistic link between alpha oscillations and DMN functioning. That transcranial alpha modulation can up-regulate the DMN further highlights an effective noninvasive intervention to normalize DMN functioning in various disorders.

The Microstructure of Attentional Control in the Dorsal Attention Network.

The top-down control of attention involves command signals arising chiefly in the dorsal attention network (DAN) in frontal and parietal cortex and propagating to sensory cortex to enable the selective processing of incoming stimuli based on their behavioral relevance. Consistent with this view, the DAN is active during preparatory (anticipatory) attention for relevant events and objects, which, in vision, may be defined by different stimulus attributes including their spatial location, color, motion, or form. How this network is organized to support different forms of preparatory attention to different stimulus attributes remains unclear. We propose that, within the DAN, there exist functional microstructures (patterns of activity) specific for controlling attention based on the specific information to be attended. To test this, we contrasted preparatory attention to stimulus location (spatial attention) and to stimulus color (feature attention), and used multivoxel pattern analysis to characterize the corresponding patterns of activity within the DAN. We observed different multivoxel patterns of BOLD activation within the DAN for the control of spatial attention (attending left vs. right) and feature attention (attending red vs. green). These patterns of activity for spatial and feature attentional control showed limited overlap with each other within the DAN. Our findings thus support a model in which the DAN has different functional microstructures for distinctive forms of top-down control of visual attention.

Role of Inferior Frontal Junction (IFJ) in the Control of Feature versus Spatial Attention.

Feature-based visual attention refers to preferential selection and processing of visual stimuli based on their nonspatial attributes, such as color or shape. Recent studies have highlighted the inferior frontal junction (IFJ) as a control region for feature but not spatial attention. However, the extent to which IFJ contributes to spatial versus feature attention control remains a topic of debate. We investigated in humans of both sexes the role of IFJ in the control of feature versus spatial attention in a cued visual spatial (attend-left or attend-right) and feature (attend-red or attend-green) attention task using fMRI. Analyzing cue-related fMRI using both univariate activation and multivoxel pattern analysis, we found the following results in IFJ. First, in line with some prior studies, the univariate activations were not different between feature and spatial attentional control. Second, in contrast, the multivoxel pattern analysis decoding accuracy was above chance level for feature attention (attend-red vs attend-green) but not for spatial attention (attend-left vs attend-right). Third, while the decoding accuracy for feature attention was above chance level during attentional control in the cue-to-target interval, it was not during target processing. Fourth, the right IFJ and visual cortex (V4) were observed to be functionally connected during feature but not during spatial attention control, and this functional connectivity was positively associated with subsequent attentional selection of targets in V4, as well as with behavioral performance. These results support a model in which IFJ plays a crucial role in top-down control of visual feature but not visual spatial attention.SIGNIFICANCE STATEMENT Past work has shown that the inferior frontal junction (IFJ), a prefrontal structure, is activated by both attention-to-feature (e.g., color) and attention-to-location, but the precise role of IFJ in the control of feature- versus spatial-attention is debated. We investigated this issue in a cued visual spatial (attend-left or attend-right) and feature (attend-red or attend-green) attention task using fMRI, multivoxel pattern analysis, and functional connectivity methods. The results show that (1) attend-red versus attend-green can be decoded in single-trial cue-evoked BOLD activity in IFJ but not attend-left versus attend-right and (2) only right IFJ modulates V4 to enhance task performance. This study sheds light on the function and hemispheric specialization of IFJ in the control of visual attention.