Arnaud Boré

Tractometer: Towards validation of tractography pipelines

We have developed the Tractometer: an online evaluation and validation system for tractography processing pipelines. One can now evaluate the results of more than 57,000 fiber tracking outputs using different acquisition settings (b-value, averaging), different local estimation techniques (tensor, q-ball, spherical deconvolution) and different tracking parameters (masking, seeding, maximum curvature, step size). At this stage, the system is solely based on a revised FiberCup analysis, but we hope that the community will get involved and provide us with new phantoms, new algorithms, third party libraries and new geometrical metrics, to name a few. We believe that the new connectivity analysis and tractography characteristics proposed can highlight limits of the algorithms and contribute in solving open questions in fiber tracking: from raw data to connectivity analysis. Overall, we show that (i) averaging improves quality of tractography, (ii) sharp angular ODF profiles helps tractography, (iii) seeding and multi-seeding has a large impact on tractography outputs and must be used with care, and (iv) deterministic tractography produces less invalid tracts which leads to better connectivity results than probabilistic tractography.

The challenge of mapping the human connectome based on diffusion tractography

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.

Tractography-based connectomes are dominated by false-positive connections

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

Tractography in the study of the human brain: a neurosurgical perspective.

BACKGROUND The brain functions as an integrated multi-networked organ. Complex neurocognitive functions are not attributed to a single brain area but depend on the dynamic interactions of distributed brain areas operating in large-scale networks. This is especially important in the field of neurosurgery where intervention within a spatially localized area may indirectly lead to unwanted effects on distant areas. As part of a preliminary integrated work on functional connectivity, we present our initial work on diffusion tensor imaging tractography to produce in vivo white matter tracts dissection. METHODS Diffusion weighted data and high-resolution T1- weighted images were acquired from a healthy right-handed volunteer (25 years old) on a whole-body 3 T scanner. Two approaches were used to dissect the tractography results: 1) a standard region of interest technique and 2) the use of Brodmann’s area as seeding points, which represents an innovation in terms of seeds initiation. RESULTS Results are presented as tri-dimensional tractography images. The uncinate fasciculus, the inferior longitudinal fasciculus, the inferior fronto-occipital fasiculus, the corticospinal tract, the corpus callosum, the cingulum, and the optic radiations where studied by conventional seeding approach, while Broca’s and Wernicke’s areas, the primary motor as well as the primary visual cortices were used as seeding areas in the second approach. CONCLUSIONS We report state-of-the-art tractography results of some of the major white matter bundles in a normal subject using DTI. Moreover, we used Brodmann’s area as seeding areas for fiber tracts to study the connectivity of known major functional cortical areas.

Tractometer: online evaluation system for tractography

We have developed a tractometer: an online evaluation system for tractography processing pipelines. One can now evaluate the end effects on fiber tracts of different acquisition parameters (b-value, number of directions, denoising or not, averaging or not), different local estimation techniques (tensor, q-ball, spherical deconvolution, spherical wavelets) and to different tractography parameters (masking, seeding, stopping criteria). At this stage, the system is solely based on a revised FiberCup analysis, but we hope that the community gets involved and provides us with new phantoms, new algorithms, third party libraries and new geometrical metrics, to name a few. We believe that the new connectivity analysis and tractography characteristics proposed can highlight limits of the algorithms and contribute in elucidating the open questions in fiber tracking: from raw data to connectivity analysis.

Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization

A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing “background” sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters. Complex demodulation was used to extract instantaneous power in the spindle band. To account for intra- and inter-individual differences, the signal was z-score transformed using a 60 s sliding window, per channel, over the course of the recording. Spindle events were detected with a z-score threshold corresponding to a low probability (e.g., 99th percentile). Spindle characteristics, such as amplitude, duration and oscillatory frequency, were derived for each individual spindle following detection, which permits spindles to be subsequently and flexibly categorized as slow or fast spindles from a single detection pass. Spindles were automatically detected in 15 young healthy subjects. Two experts manually identified spindles from C3 during Stage 2 sleep, from each recording; one employing conventional guidelines, and the other, identifying spindles with the aid of a sigma (11–16 Hz) filtered channel. These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives. This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles.

Reactivation or transformation? Motor memory consolidation associated with cerebral activation time-locked to sleep spindles

Motor memory consolidation is thought to depend on sleep-dependent reactivation of brain areas recruited during learning. However, up to this point, there has been no direct evidence to support this assertion in humans, and the physiological processes supporting such reactivation are unknown. Here, simultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were conducted during post-learning sleep to directly investigate the spindle-related reactivation of a memory trace formed during motor sequence learning (MSL), and its relationship to overnight enhancement in performance (reflecting consolidation). We show that brain regions within the striato-cerebello-cortical network recruited during training on the MSL task, and in particular the striatum, were also activated during sleep, time-locked to spindles. Interestingly, the consolidated trace in the striatum was not simply strengthened, but was transformed/reorganized from rostrodorsal (associative) to caudoventral (sensorimotor) subregions. Moreover, the degree of the reactivation was correlated with overnight improvements in performance. Altogether, the present findings demonstrate that striatal reactivation linked to sleep spindles in the post-learning night, is related to motor memory consolidation.

Transient synchronization of hippocampo-striato-thalamo-cortical networks during sleep spindle oscillations induces motor memory consolidation

ABSTRACT Sleep benefits motor memory consolidation. This mnemonic process is thought to be mediated by thalamo‐cortical spindle activity during NREM‐stage2 sleep episodes as well as changes in striatal and hippocampal activity. However, direct experimental evidence supporting the contribution of such sleep‐dependent physiological mechanisms to motor memory consolidation in humans is lacking. In the present study, we combined EEG and fMRI sleep recordings following practice of a motor sequence learning (MSL) task to determine whether spindle oscillations support sleep‐dependent motor memory consolidation by transiently synchronizing and coordinating specialized cortical and subcortical networks. To that end, we conducted EEG source reconstruction on spindle epochs in both cortical and subcortical regions using novel deep‐source localization techniques. Coherence‐based metrics were adopted to estimate functional connectivity between cortical and subcortical structures over specific frequency bands. Our findings not only confirm the critical and functional role of NREM‐stage2 sleep spindles in motor skill consolidation, but provide first‐time evidence that spindle oscillations [11–17 Hz] may be involved in sleep‐dependent motor memory consolidation by locally reactivating and functionally binding specific task‐relevant cortical and subcortical regions within networks including the hippocampus, putamen, thalamus and motor‐related cortical regions.

A 12-Week Cycling Training Regimen Improves Gait and Executive Functions Concomitantly in People with Parkinson’s Disease

Background: There is increasing evidence that executive functions and attention are associated with gait and balance, and that this link is especially prominent in older individuals or those who are afflicted by neurodegenerative diseases that affect cognition and/or motor functions. People with Parkinson’s disease (PD) often present gait disturbances, which can be reduced when PD patients engage in different types of physical exercise (PE), such as walking on a treadmill. Similarly, PE has also been found to improve executive functions in this population. Yet, no exercise intervention investigated simultaneously gait and non-motor symptoms (executive functions, motor learning) in PD patients. Objective: To assess the impact of aerobic exercise training (AET) using a stationary bicycle on a set of gait parameters (walking speed, cadence, step length, step width, single and double support time, as well as variability of step length, step width and double support time) and executive functions (cognitive inhibition and flexibility) in sedentary PD patients and healthy controls. Methods: Two groups, 19 PD patients (Hoehn and Yahr ≤2) and 20 healthy adults, matched on age and sedentary level, followed a 3-month stationary bicycle AET regimen. Results: Aerobic capacity, as well as performance of motor learning and on cognitive inhibition, increased significantly in both groups after the training regimen, but only PD patients improved their walking speed and cadence (all p < 0.05; with no change in the step length). Moreover, in PD patients, training-related improvements in aerobic capacity correlated positively with improvements in walking speed (r = 0.461, p < 0.05). Conclusion: AET using stationary bicycle can independently improve gait and cognitive inhibition in sedentary PD patients. Given that increases in walking speed were obtained through increases in cadence, with no change in step length, our findings suggest that gait improvements are specific to the type of motor activity practiced during exercise (i.e., pedaling). In contrast, the improvements seen in cognitive inhibition were, most likely, not specific to the type of training and they could be due to indirect action mechanisms (i.e., improvement of cardiovascular capacity). These results are also relevant for the development of targeted AET interventions to improve functional autonomy in PD patients.

Age-related white-matter correlates of motor sequence learning and consolidation

Older adults show impaired consolidation in motor sequence learning (MSL) tasks, failing to demonstrate the sleep-dependent performance gains usually seen in young individuals. To date, few studies have investigated the white-matter substrates of MSL in healthy aging, and none have addressed how fiber pathways differences may contribute to the age-related consolidation deficit. Accordingly, we used diffusion-weighted magnetic resonance imaging to explore how white-matter characteristics relate to performance using an explicit MSL task in young and older participants. Analysis revealed that initial learning scores were correlated to white-matter microstructure in the corticospinal tract and within the corpus callosum regardless of age. Furthermore, sleep-dependent consolidation scores, in young adults only, were related to white-matter tract organization in a frontal area where several major fiber bundles cross each other. These findings further our understanding of the neural correlates of MSL in healthy aging and provide the first evidence that age-related white-matter differences in tract configuration may underlie the age-related motor memory consolidation deficit.