There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.

The predominant role of the primary motor cortex (M1) in motor execution is well acknowledged. However, additional roles of M1 are getting evident in humans owing to advances in noninvasive brain stimulation (NIBS) techniques. This review collates such studies in humans and proposes that M1 also plays a key role in higher cognitive processes. The review commences with the studies that have investigated the nature of connectivity of M1 with other cortical regions in light of studies based on NIBS. The review then moves on to discuss the studies that have demonstrated the role of M1 in higher cognitive processes such as attention, motor learning, motor consolidation, movement inhibition, somatomotor response, and movement imagery. Overall, the purpose of the review is to highlight the additional role of M1 in motor cognition besides motor control, which remains unexplored.

Modulating higher cognitive functions like reading with transcranial direct current stimulation (tDCS) can be challenging as reading involves regions in the dorsal and ventral cortical areas that lie in close proximity. If the two pathways are stimulated simultaneously, the function of dorsal pathway (predominantly used for graphophonological conversion) might interfere with the function of the ventral pathway (used for semantics), and vice-versa. To achieve functional specificity in tDCS for investigating the two pathways of reading, it is important to stimulate each pathway per session such that the spread of current across the cortical areas due to the two montages has minimal overlap. The present study intends to achieve this by introducing a systematic approach for tDCS analysis. We employed the COMETS2 software to simulate 10 montage configurations (5 for each pathway) for three electrode sizes: 5 × 5, 3 × 3, and 5 × 7 cm 2 . This diversity in montage configuration is chosen since previous studies found the position and the size of anode and cathode to play an important role. The values of the magnitude of current density (MCD) obtained from the configuration were used to calculate: (i) average MCD in each cortical lobe, (ii) number of overlapping coordinates, and (iii) cortical areas with high MCD. The measures (i) and (iii) ascertained the current spread by each montage within a cortical lobe, and (ii) verified the overlap of the spread of current between a pair of montages. The analyses show that a montage using the electrode size of 5 × 5 cm 2 with the anode at CP5 and cathode at CZ, and another with anode at TP7 and cathode at nape of the neck are optimal choices for dorsal and ventral pathways, respectively. To verify, we cross-validated the results with ROAST. This systematic approach was helpful in reducing the ambiguity of montage selection prior to conducting a tDCS study.

Background: In transcranial direct current stimulation (tDCS), the injected current becomes distributed across the brain areas. The objective is to stimulate the target region of interest (ROI) while minimizing the current in non-target ROIs (the ‘focality’ of tDCS). For this purpose, determining the appropriate current dose for an individual is difficult. Aim: To introduce a dose–target determination index (DTDI) to quantify the focality of tDCS and examine the dose–focality relationship in three different populations. Method: Here, we extended our previous toolbox i-SATA to the MNI reference space. After a tDCS montage is simulated for a current dose, the i-SATA(MNI) computes the average (over voxels) current density for every region in the brain. DTDI is the ratio of the average current density at the target ROI to the ROI with a maximum value (the peak region). Ideally, target ROI should be the peak region, so DTDI shall range from 0 to 1. The higher the value, the better the dose. We estimated the variation of DTDI within and across individuals using T1-weighted brain images of 45 males and females distributed equally across three age groups: (a) young adults (20 ≤ x ˂ 40 years), (b) mid adults (40 ≤ x ˂ 60 years), and (c) older adults (60 ≤ x ˂ 80 years). DTDI’s were evaluated for the frontal montage with electrodes at F3 and the right supraorbital for three current doses of 1 mA, 2 mA, and 3 mA, with the target ROI at the left middle frontal gyrus. Result: As the dose is incremented, DTDI may show (a) increase, (b) decrease, and (c) no change across the individuals depending on the relationship (nonlinear or linear) between the injected tDCS current and the distribution of current density in the target ROI. The nonlinearity is predominant in older adults with a decrease in focality. The decline is stronger in males. Higher current dose at older age can enhance the focality of stimulation. Conclusion: DTDI provides information on which tDCS current dose will optimize the focality of stimulation. The recommended DTDI dose should be prioritized based on the age (>40 years) and sex (especially for males) of an individual. The toolbox i-SATA(MNI) is freely available.

Background: Transcranial Direct Current Stimulation (tDCS) is a technique where a weak current is passed through the electrodes placed on the scalp. The distribution of the electric current induced in the brain due to tDCS is provided by simulation toolbox like Realisticvolumetric-Approach-based-Simulator-for-Transcranial-electric-stimulation (ROAST).However, the procedure to estimate the total current density induced at the target and the intermediary region of the cortex is complex. The Systematic-Approach-for-tDCS-Analysis (SATA) was developed to overcome this problem. However, SATA is limited to standardized headspace only. Here we develop individual-SATA (भ-SATA) to extend it to individual head. Method: T1-weighted images of 15 subjects were taken from two Magnetic Resonance Imaging (MRI) scanners of different strengths. Across the subjects, the montages were simulated in ROAST. भ -SATA converts the ROAST output to Talairach space. The x, y and z coordinates of the anterior commissure (AC), posterior commissure (PC), and Mid-Sagittal (MS) points are necessary for the conversion. AC and PC are detected using the acpcdetect toolbox. We developed a method to determine the MS in the image and cross-verified its location manually using BrainSight®. Result: Determination of points with भ -SATA is fast and accurate. The भ -SATA provided estimates of the current-density induced across an individual's cortical lobes and gyri as tested on images from two different scanners. Conclusion: Researchers can use भ -SATA for customizing tDCS-montages. With भ -SATA it is also easier to compute the inter-individual variation in current-density across the target and intermediary regions of the brain. The software is publicly available.

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There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.

No abstract