Dissection of the Temporofrontal Extreme Capsule Fasciculus Using Diffusion MRI Tractography and Association with Lexical Retrieval.

The well-known arcuate fasciculus that connects the posterior superior temporal region with the language production region in the ventrolateral frontal cortex constitutes the classic peri-Sylvian dorsal stream of language. A second temporofrontal white matter tract connects ventrally the anterior to intermediate lateral temporal cortex with frontal areas via the extreme capsule. This temporofrontal extreme capsule fasciculus (TFexcF) constitutes the ventral stream of language processing. The precise origin, course, and termination of this pathway has been examined in invasive tract tracing studies in macaque monkeys, but there have been no standard protocols for its reconstruction in the human brain using diffusion imaging tractography. Here we provide a protocol for the dissection of the TFexcF in vivo in the human brain using diffusion magnetic resonance imaging (MRI) tractography which provides a solid basis for exploring its functional role. A key finding of the current dissection protocol is the demonstration that the TFexcF is left hemisphere lateralized. Furthermore, using the present dissection protocol, we demonstrate that the TFexcF is related to lexical retrieval scores measured with the category fluency test, in contrast to the classical arcuate fasciculus (the dorsal language pathway) that was also dissected and was related to sentence repetition.

The Three Terms Task - an open benchmark to compare human and artificial semantic representations.

Word processing entails retrieval of a unitary yet multidimensional semantic representation (e.g., a lemon's colour, flavour, possible use) and has been investigated in both cognitive neuroscience and artificial intelligence. To enable the direct comparison of human and artificial semantic representations, and to support the use of natural language processing (NLP) for computational modelling of human understanding, a critical challenge is the development of benchmarks of appropriate size and complexity. Here we present a dataset probing semantic knowledge with a three-terms semantic associative task: which of two target words is more closely associated with a given anchor (e.g., is lemon closer to squeezer or sour?). The dataset includes both abstract and concrete nouns for a total of 10,107 triplets. For the 2,255 triplets with varying levels of agreement among NLP word embeddings, we additionally collected behavioural similarity judgments from 1,322 human raters. We hope that this openly available, large-scale dataset will be a useful benchmark for both computational and neuroscientific investigations of semantic knowledge.

Rostral-Caudal Hippocampal Functional Convergence Is Reduced Across the Alzheimer's Disease Spectrum.

Beginning in the early stages of Alzheimer's disease (AD), the hippocampus reduces its functional connections to other cortical regions due to synaptic depletion. However, little is known regarding connectivity abnormalities within the hippocampus. Here, we describe rostral-caudal hippocampal convergence (rcHC), a metric of the overlap between the rostral and caudal hippocampal functional networks, across the clinical spectrum of AD. We predicted a decline in rostral-caudal hippocampal convergence in the early stages of the disease. Using fMRI, we generated resting-state hippocampal functional networks across 56 controls, 48 early MCI (EMCI), 35 late MCI (LMCI), and 31 AD patients from the Alzheimer's Disease Neuroimaging Initiative cohort. For each diagnostic group, we performed a conjunction analysis and compared the rostral and caudal hippocampal network changes using a mixed effects linear model to estimate the convergence and differences between these networks, respectively. The conjunction analysis showed a reduction of rostral-caudal hippocampal convergence strength from early MCI to AD, independent of hippocampal atrophy. Our results demonstrate a parallel between the functional convergence within the hippocampus and disease stage, which is independent of brain atrophy. These findings support the concept that network convergence might contribute as a biomarker for connectivity dysfunction in early stages of AD.

Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state.

The effect of regressing out the global average signal (GAS) in resting state fMRI data has become a concern for interpreting functional connectivity analyses. It is not clear whether the reported anti-correlations between the Default Mode and the Dorsal Attention Networks are intrinsic to the brain, or are artificially created by regressing out the GAS. Here we introduce a concept, Impact of the Global Average on Functional Connectivity (IGAFC), for quantifying the sensitivity of seed-based correlation analyses to the regression of the GAS. This voxel-wise IGAFC index is defined as the product of two correlation coefficients: the correlation between the GAS and the fMRI time course of a voxel, times the correlation between the GAS and the seed time course. This definition enables the calculation of a threshold at which the impact of regressing-out the GAS would be large enough to introduce spurious negative correlations. It also yields a post-hoc impact correction procedure via thresholding, which eliminates spurious correlations introduced by regressing out the GAS. In addition, we introduce an Artificial Negative Correlation Index (ANCI), defined as the absolute difference between the IGAFC index and the impact threshold. The ANCI allows a graded confidence scale for ranking voxels according to their likelihood of showing artificial correlations. By applying this method, we observed regions in the Default Mode and Dorsal Attention Networks that were anti-correlated. These findings confirm that the previously reported negative correlations between the Dorsal Attention and Default Mode Networks are intrinsic to the brain and not the result of statistical manipulations. Our proposed quantification of the impact that a confound may have on functional connectivity can be generalized to global effect estimators other than the GAS. It can be readily applied to other confounds, such as systemic physiological or head movement interferences, in order to quantify their impact on functional connectivity in the resting state.

Task-related BOLD responses and resting-state functional connectivity during physiological clamping of end-tidal CO(2).

Carbon dioxide (CO(2)), a potent vasodilator, is known to have a significant impact on the blood-oxygen level dependent (BOLD) signal. With the growing interest in studying synchronized BOLD fluctuations during the resting state, the extent to which the apparent synchrony is due to variations in the end-tidal pressure of CO(2) (PETCO(2)) is an important consideration. CO(2)-related fluctuations in BOLD signal may also represent a potential confound when studying task-related responses, especially if breathing depth and rate are affected by the task. While previous studies of the above issues have explored retrospective correction of BOLD fluctuations related to arterial PCO(2), here we demonstrate an alternative approach based on physiological clamping of the arterial CO(2) level to a near-constant value. We present data comparing resting-state functional connectivity within the default-mode-network (DMN), as well as task-related BOLD responses, acquired in two conditions in each subject: 1) while subject's PETCO(2) was allowed to vary spontaneously; and 2) while controlling subject's PETCO(2) within a narrow range. Strong task-related responses and areas of maximal signal correlation in the DMN were not significantly altered by suppressing fluctuations in PETCO(2). Controlling PETCO(2) did, however, improve the performance of retrospective physiological noise correction techniques, allowing detection of additional regions of task-related response and resting-state connectivity in highly vascularized regions such as occipital cortex. While these results serve to further rule out systemic physiological fluctuations as a significant source of apparent resting-state network connectivity, they also demonstrate that fluctuations in arterial CO(2) are one of the factors limiting sensitivity in task-based and resting-state fMRI, particularly in regions of high vascular density. This must be considered when comparing subject groups who might exhibit differences in respiratory physiology or breathing patterns.

Functional neuroanatomy associated with the expression of distinct movement kinematics in motor sequence learning.

A broad range of motor skills, such as speech and writing, evolves with the ability to articulate elementary motor movements into novel sequences that come to be performed smoothly through practice. Neuroimaging studies in humans have demonstrated the involvement of the cerebello-cortical and striato-cortical motor loops in the course of motor sequence learning. Nonetheless, the nature of the improvement and brain mechanisms underlying different parameters of movement kinematics are not yet fully ascertained. We aimed at dissociating the cerebral substrates related to the increase in performance on two kinematic indices: velocity, that is the speed with which each single movement in the sequence is produced, and transitions, that is the duration of the gap between these individual movements. In this event-related fMRI experiment, participants practiced an eight-element sequence of finger presses on a keypad which allowed to record those kinematic movement parameters. Velocity was associated with activations in the ipsilateral spinocerebellum (lobules 4-5, 8 and medial lobule 6) and in the contralateral primary motor cortex. Transitions were associated with increased activity in the neocerebellum (lobules 6 bilaterally and lobule 4-5 ipsilaterally), as well as with activations within the right and left putamen and a broader bilateral network of motor cortical areas. These findings indicate that, rather than being the product of a single mechanism, the general improvement in motor performance associated with early motor sequence learning arises from at least two distinct kinematic processes, whose behavioral expressions are supported by partially overlapping and segregated brain networks.

Accurate anisotropic fast marching for diffusion-based geodesic tractography.

Using geodesics for inferring white matter fibre tracts from diffusion-weighted MR data is an attractive method for at least two reasons: (i) the method optimises a global criterion, and hence is less sensitive to local perturbations such as noise or partial volume effects, and (ii) the method is fast, allowing to infer on a large number of connexions in a reasonable computational time. Here, we propose an improved fast marching algorithm to infer on geodesic paths. Specifically, this procedure is designed to achieve accurate front propagation in an anisotropic elliptic medium, such as DTI data. We evaluate the numerical performance of this approach on simulated datasets, as well as its robustness to local perturbation induced by fiber crossing. On real data, we demonstrate the feasibility of extracting geodesics to connect an extended set of brain regions.