MS optic neuritis-induced long-term structural changes within the visual pathway.

BACKGROUND: The visual pathway is commonly involved in multiple sclerosis (MS), even in its early stages, including clinical episodes of optic neuritis (ON). The long-term structural damage within the visual compartment in patients with ON, however, is yet to be elucidated. OBJECTIVE: Our aim was to characterize visual system structure abnormalities using MRI along with optical coherence tomography (OCT) and pattern-reversal visual evoked potentials (VEPs) depending on a single history of ON. METHODS: Twenty-eight patients with clinically definitive MS, either with a history of a single ON (HON) or without such history and normal VEP findings (NON), were included. OCT measures comprised OCT-derived peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell/inner plexiform layer (GCIPL) thickness. Cortical and global gray and white matter, thalamic, and T2 lesion volumes were assessed using structural MRI. Diffusion-weighted MRI-derived measures included fractional anisotropy (FA), mean (MD), radial (RD), and axial (AD) diffusivity within the optic radiation (OR). RESULTS: Mean (SD) duration after ON was 8.3 (3.7) years. Compared with the NON group, HON patients showed significant RNFL (p = 0.01) and GCIPL thinning (p = 0.002). OR FA (p = 0.014), MD (p = 0.005), RD (p = 0.007), and AD (p = 0.004) were altered compared with NON. Global gray and white as well as other regional gray matter structures did not differ between the 2 groups. CONCLUSION: A single history of ON induces long-term structural damage within the retina and OR suggestive of both retrograde and anterograde neuroaxonal degeneration.

Electroencephalography reveals a selective disruption of cognitive control processes in craving cigarette smokers.

Addiction to nicotine is extremely challenging to overcome, and the intense craving for the next cigarette often leads to relapse in smokers who wish to quit. To dampen the urges of craving and inhibit unwanted behaviour, smokers must harness cognitive control, which is itself impaired in addiction. It is likely that craving may interact with cognitive control, and the present study sought to test the specificity of such interactions. To this end, data from 24 smokers were gathered using EEG and behavioural measures in a craving session (following a three-hour nicotine abstention period) and a non-craving session (having just smoked). In both sessions, participants performed a task probing various facets of cognitive control (response inhibition, task switching and conflict processing). Results showed that craving smokers were less flexible with the implementation of cognitive control, with demands of task switching and incongruency yielding greater deficits under conditions of craving. Importantly, inhibitory control was not affected by craving, suggesting that the interactions of craving and cognitive control are selective. Together, these results provide evidence that smokers already exhibit specific control-related deficits after brief nicotine deprivation. This disruption of cognitive control while craving may help to explain why abstinence is so difficult to maintain.

Cortical, subcortical and spinal neural correlates of slackline training-induced balance performance improvements.

Humans develop posture and balance control during childhood. Interestingly, adults can also learn to master new complex balance tasks, but the underlying neural mechanisms are not fully understood yet. Here, we combined broad scale brain connectivity fMRI at rest and spinal excitability measurements during movement. Six weeks of slackline training improved the capability to walk on a slackline which was paralleled by functional connectivity changes in brain regions associated with posture and balance control and by task-specific changes of spinal excitability. Importantly, the performance of trainees was not better than control participants in a different, untrained balance task. In conclusion, slackline training induced large-scale neuroplasticity which solely transferred into highly task specific performance improvements.

Prefrontal cortical thickness in motor neuron disease.

Objective: To examine whether the distribution of prefrontal cortical thickness in patients with motor neuron disease is normal or bimodal and how it compares to the normal population. Methods: 158 patients with motor neuron disease (MND) and 86 healthy controls (HC) were enrolled in a prospective, two-center study with a common structural MRI protocol. Cortical thickness measures were extracted for the prefrontal cortex, premotor cortex, motor cortex, and occipital cortex using FreeSurfer, adjusted for age and sex, and tested for normality of distribution. Results: Cortical thickness measures of the bilateral prefrontal, premotor, motor, and occipital cortex were normally distributed in patients and healthy controls. MND-related cortical thinning was observed in the right motor cortex (p = 0.002), reflected in a significantly higher proportion of MND cases being worse than -1 standard deviation of the healthy control mean: 29.1% in the right motor cortex (p = 0.002). Cortical thinning of the left motor cortex was a function of clinical phenotype and physical disability. Left prefrontal cortical thickness was reduced in patients with additional cognitive and/or behavioural deficits compared to MND patients without cognitive deficits. Prefrontal, premotor, motor, and occipital cortical thickness was related to patients' general cognitive abilities. Conclusion: The study shows that prefrontal cortical thickness in MND is normally distributed but shifted towards thinner cortex in MND patients with cognitive and/or behavioural impairment. The distribution of thickness values did not indicate the assumption of a bimodal distribution although patients with comorbid cognitive deficits are more likely to suffer from prefrontal cortical thinning.

Attention to Color Sharpens Neural Population Tuning via Feedback Processing in the Human Visual Cortex Hierarchy.

Attention can facilitate the selection of elementary object features such as color, orientation, or motion. This is referred to as feature-based attention and it is commonly attributed to a modulation of the gain and tuning of feature-selective units in visual cortex. Although gain mechanisms are well characterized, little is known about the cortical processes underlying the sharpening of feature selectivity. Here, we show with high-resolution magnetoencephalography in human observers (men and women) that sharpened selectivity for a particular color arises from feedback processing in the human visual cortex hierarchy. To assess color selectivity, we analyze the response to a color probe that varies in color distance from an attended color target. We find that attention causes an initial gain enhancement in anterior ventral extrastriate cortex that is coarsely selective for the target color and transitions within ∼100 ms into a sharper tuned profile in more posterior ventral occipital cortex. We conclude that attention sharpens selectivity over time by attenuating the response at lower levels of the cortical hierarchy to color values neighboring the target in color space. These observations support computational models proposing that attention tunes feature selectivity in visual cortex through backward-propagating attenuation of units less tuned to the target.SIGNIFICANCE STATEMENT Whether searching for your car, a particular item of clothing, or just obeying traffic lights, in everyday life, we must select items based on color. But how does attention allow us to select a specific color? Here, we use high spatiotemporal resolution neuromagnetic recordings to examine how color selectivity emerges in the human brain. We find that color selectivity evolves as a coarse to fine process from higher to lower levels within the visual cortex hierarchy. Our observations support computational models proposing that feature selectivity increases over time by attenuating the responses of less-selective cells in lower-level brain areas. These data emphasize that color perception involves multiple areas across a hierarchy of regions, interacting with each other in a complex, recursive manner.

Widespread temporo-occipital lobe dysfunction in amyotrophic lateral sclerosis.

Recent studies suggest that amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) lie on a single clinical continuum. However, previous neuroimaging studies have found only limited involvement of temporal lobe regions in ALS. To better delineate possible temporal lobe involvement in ALS, the present study aimed to examine changes in functional connectivity across the whole brain, particularly with regard to extra-motor regions, in a group of 64 non-demented ALS patients and 38 healthy controls. To assess between-group differences in connectivity, we computed edge-level statistics across subject-specific graphs derived from resting-state functional MRI data. In addition to expected ALS-related decreases in functional connectivity in motor-related areas, we observed extensive changes in connectivity across the temporo-occipital cortex. Although ALS patients with comorbid FTD were deliberately excluded from this study, the pattern of connectivity alterations closely resembles patterns of cerebral degeneration typically seen in FTD. This evidence for subclinical temporal dysfunction supports the idea of a common pathology in ALS and FTD.

Memory-Efficient Analysis of Dense Functional Connectomes.

The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download.

Basal ganglia pathology in ALS is associated with neuropsychological deficits.

OBJECTIVES: To evaluate basal ganglia changes along the amyotrophic lateral sclerosis (ALS)-ALS-frontotemporal dementia (FTD) continuum using multiple, complementary imaging techniques. METHODS: Sixty-seven C9orf72-negative patients with ALS and 39 healthy controls were included in a cross-sectional quantitative MRI study. Seven patients with ALS met criteria for comorbid behavioral variant FTD (ALS-FTD), 18 patients met the Strong criteria for cognitive and/or behavioral impairment (ALS-Plus), and 42 patients had no cognitive impairment (ALS-Nci). Volumetric, shape, and density analyses were performed for the thalamus, amygdala, nucleus accumbens, hippocampus, caudate nucleus, pallidum, and putamen. RESULTS: Significant basal ganglia volume differences were identified between the study groups. Shape analysis revealed distinct atrophy patterns in the amygdala in patients with ALS-Nci and in the hippocampus in patients with ALS-Plus in comparison with controls. Patients with ALS-FTD exhibited pathologic changes in the bilateral thalami, putamina, pallida, hippocampi, caudate, and accumbens nuclei in comparison with all other study groups. A preferential vulnerability has been identified within basal ganglia subregions, which connect directly to key cortical sites of ALS pathology. While the anatomical patterns were analogous, the degree of volumetric, shape, and density changes confirmed incremental pathology through the spectrum of ALS-Nci, ALS-Plus, to ALS-FTD. Performance on verbal memory tests correlated with hippocampal volumes, and accumbens nuclei volumes showed a negative correlation with apathy scores. CONCLUSIONS: We demonstrate correlations between basal ganglia measures and structure-specific neuropsychological performance and a gradient of incremental basal ganglia pathology across the ALS-ALS-FTD spectrum, suggesting that the degree of subcortical gray matter pathology in C9orf72-negative ALS is closely associated with neuropsychological changes.

Structural hallmarks of amyotrophic lateral sclerosis progression revealed by probabilistic fiber tractography.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive limb and/or bulbar muscular weakness and atrophy. Although ALS-related alterations of motor and extra-motor neuronal networks have repeatedly been reported, their temporal dynamics during disease progression are not well understood. Recently, we reported a decline of motor system activity and a concurrent increase of hippocampal novelty-evoked modulations across 3 months of ALS progression. To address whether these functional changes are associated with structural ones, the current study employed probabilistic fiber tractography on diffusion tensor imaging (DTI) data using a longitudinal design. Therein, motor network integrity was assessed by DTI-based tracking of the intracranial corticospinal tract, while connectivity estimates of occipito-temporal tracts (between visual and entorhinal, perirhinal or parahippocampal cortices) served to assess structural changes that could be related to the increased novelty-evoked hippocampal activity across time described previously. Complementing these previous functional observations, the current data revealed an ALS-related decrease in corticospinal tract structural connectivity compared to controls, while in contrast, visuo-perirhinal connectivity was relatively increased in the patient group. Importantly, beyond these between-group differences, a rise in the patients' occipito-temporal tract strengths occurred across a 3-month interval, while at the same time no changes in corticospinal tract connectivity were observed. In line with previously identified functional alterations, the dynamics of these structural changes suggest that the affection of motor- and memory-related networks in ALS emerges at distinct disease stages: while motor network degeneration starts primarily during early (supposedly pre-symptomatic) phases, the hippocampal/medial temporal lobe dysfunctions arise at later stages of the disease.

Fast construction of voxel-level functional connectivity graphs.

BACKGROUND: Graph-based analysis of fMRI data has recently emerged as a promising approach to study brain networks. Based on the assessment of synchronous fMRI activity at separate brain sites, functional connectivity graphs are constructed and analyzed using graph-theoretical concepts. Most previous studies investigated region-level graphs, which are computationally inexpensive, but bring along the problem of choosing sensible regions and involve blurring of more detailed information. In contrast, voxel-level graphs provide the finest granularity attainable from the data, enabling analyses at superior spatial resolution. They are, however, associated with considerable computational demands, which can render high-resolution analyses infeasible. In response, many existing studies investigating functional connectivity at the voxel-level reduced the computational burden by sacrificing spatial resolution. METHODS: Here, a novel, time-efficient method for graph construction is presented that retains the original spatial resolution. Performance gains are instead achieved through data reduction in the temporal domain based on dichotomization of voxel time series combined with tetrachoric correlation estimation and efficient implementation. RESULTS: By comparison with graph construction based on Pearson's r, the technique used by the majority of previous studies, we find that the novel approach produces highly similar results an order of magnitude faster. CONCLUSIONS: Its demonstrated performance makes the proposed approach a sensible and efficient alternative to customary practice. An open source software package containing the created programs is freely available for download.