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Alzheimer’s disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient’s autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the ‘cognitive brain wave’ P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels. A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach. In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

Although measurement of total hippocampal volume is considered as an important hallmark of Alzheimer's disease (AD), recent evidence demonstrated that atrophies of hippocampal subregions might be more sensitive in predicting this neurodegenerative disease. The vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will or not convert in AD. For this reason, the aim of this study was to determine if measurements of hippocampal subfields provide advantages over total hippocampal volume for discriminating these groups. Hippocampal subfields volumetry was extracted in 55 AD, 32 converted and 89 not-converted MCI (c/nc-MCI) and 47 healthy controls, using an atlas-based automatic algorithm based on Markov random fields embedded in the Freesurfer framework. To evaluate the impact of hippocampal atrophy in discriminating the insurgence of AD-like phenotypes we used three classification methods: Support Vector Machine, Naïve Bayesian Classifier and Neural Networks Classifier. Taking into account only the total hippocampal volume, all classification models, reached a sensitivity of about 66% in discriminating between c-MCI and nc-MCI. Otherwise, classification analysis considering all segmenting subfields increased accuracy to diagnose c-MCI from 68% to 72%. This effect resulted to be strongly dependent upon atrophies of the subiculum and presubiculum. Our multivariate analysis revealed that the magnitude of the difference considering hippocampal subfield volumetry, as segmented by the considered atlas-based automatic algorithm, offers an advantage over hippocampal volume in distinguishing early AD from nc-MCI.

The people with multiple sclerosis (MS) often report that fatigue restricts their life. Nowadays, pharmacological treatments are poorly effective accompanied by relevant side effects. A 5-day transcranial direct current stimulation (tDCS) targeting the somatosensory representation of the whole body (S1) delivered through an electrode personalized based on the brain MRI was efficacious against MS fatigue (FaReMuS treatment). This proof of principle study tested whether possible changes of the functional organization of the primary sensorimotor network induced by FaReMuS partly explained the effected fatigue amelioration. We measured the brain activity at rest through electroencephalography equipped with a Functional Source Separation algorithm and we assessed the neurodynamics state of the primary somatosensory (S1) and motor (M1) cortices via the Fractal Dimension and their functional connectivity via the Mutual Information. The dynamics of the neuronal electric activity, more distorted in S1 than M1 before treatment, as well as the network connectivity, altered maximally between left and right M1 homologs, reverted to normal after FaReMuS. The intervention-related changes explained 48% of variance of fatigue reduction in the regression model. A personalized neuromodulation tuned in on specific anatomo-functional features of the impaired regions can be effective against fatigue.

Multiple sclerosis is an autoimmune disease predominantly affecting the white matter of the CNS, causing--among functional sequelae-cortico--cortical partial or total disconnection. Since functional connectivity linking cerebral regions is reliably reflected by synchronization of their neuronal firing, in this study an electrophysiological parameter measured by magnetoencephalography was used to quantify an intra-cortical connectivity (ICC) index focused on the primary somatosensory cortical areas (S1). Twenty-one patients affected by mild (Extended Disability Scale Score, median 1,5) relapsing-remitting (RR) multiple sclerosis in the remitting phase without clinically evident sensory impairment were evaluated. Three dimensional MRI was used to quantify the lesion load, discriminating black hole and non-black hole portions, normalized by individual brain volumes. When matched with a control population, multiple sclerosis patients showed a reduced ICC combined with the complete loss of the finger-dependent functional specialization in S1 cortex of the dominant hemisphere. No association was found between ICC impairment and disease duration, or prolongation of the central sensory conduction time, presence of spinal cord lesions and ongoing disease modifying therapy. The ICC index slightly correlated with the lesion load. A local index of ICC in a circumscribed brain primary area was altered in mildly disabled RR-multiple sclerosis patients, also in absence of any impairment of central sensory conduction. In conclusion, the diffuse damage influencing the multi-nodal network subtending complex cerebral functions also affects intrinsic cortical connectivity. The S1 ICC index is proposed as a highly sensitive and simple-to-test functional measure for the evaluation of intra-cortical synchronization mechanisms in RR-multiple sclerosis.

Background In North America, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has established a platform to track the brain changes of Alzheimer's disease. A pilot study has been carried out in Europe to test the feasibility of the adoption of the ADNI platform (pilot E-ADNI). Methods Seven academic sites of the European Alzheimer's Disease Consortium (EADC) enrolled 19 patients with mild cognitive impairment (MCI), 22 with AD, and 18 older healthy persons by using the ADNI clinical and neuropsychological battery. ADNI compliant magnetic resonance imaging (MRI) scans, cerebrospinal fluid, and blood samples were shipped to central repositories. Medial temporal atrophy (MTA) and white matter hyperintensities (WMH) were assessed by a single rater by using visual rating scales. Results Recruitment rate was 3.5 subjects per month per site. The cognitive, behavioral, and neuropsychological features of the European subjects were very similar to their U.S. counterparts. Three-dimensional T1-weighted MRI sequences were successfully performed on all subjects, and cerebrospinal fluid samples were obtained from 77%, 68%, and 83% of AD patients, MCI patients, and controls, respectively. Mean MTA score showed a significant increase from controls (left, right: 0.4, 0.3) to MCI patients (0.9, 0.8) to AD patients (2.3, 2.0), whereas mean WMH score did not differ among the three diagnostic groups (between 0.7 and 0.9). The distribution of both MRI markers was comparable to matched US-ADNI subjects. Conclusions Academic EADC centers can adopt the ADNI platform to enroll MCI and AD patients and older controls with global cognitive and structural imaging features remarkably similar to those of the US-ADNI.

Rule-based behavior associating nonspatial visual stimuli with learned responses is called arbitrary visuomotor mapping, an ability that enriches behavioral repertoire. To better understand the underlying neural correlates, the present functional magnetic resonance imaging (fMRI) study explored brain activity during visually informed movement involving two different types of cues and two different effectors. After being trained on the tasks, six healthy subjects performed right or left finger tapping tasks according to either arbitrary cues or spatially constrained cues. An event-related fMRI experiment was conducted on a 3-T MRI. The image data were analyzed with statistical parametric mapping. With the aid of the probabilistic architectonic map in the stereotaxic space, we identified three types of task-related brain activity: cue-selective, effector-selective, and nonselective. The left ventrolateral prefrontal cortex and the rostral part of the right dorsal premotor cortex (PMd) exhibited cue-selective activity, which was greater during the arbitrary condition than the spatially constrained condition. The left ventral prefrontal activity may reflect retrieval of visuomotor association from memory in arbitrary context. The rostral part of the left PMd showed nonselective activity while the caudal part of the PMd on each side showed conspicuous effector-selective activity to the contralateral movement. These findings suggest functional demarcation of the PMd between its rostral and dorsal parts during visuomotor mapping.

Objective Fatigue in Multiple Sclerosis (MS) is a highly disabling symptom affecting social relationships and daily cognitive and physical activities of patients, being complained by up to 90% of MS sufferers. Among the central mechanisms behind it, an involvement of sensorimotor networks is evident from structural and functional studies. We aimed at assessing whether functional/structural balances of homologous sensorimotor regions – known to be crucial for sensorimotor networks effectiveness – decrease with MS fatigue increase. Methods Functional connectivity measures at rest and during a simple motor task (weak handgrip of either the right or left hand) were derived from primary sensorimotor areas EEG recordings in twenty-seven mildly disabled MS patients. Structural MRI-derived inter-hemispheric asymmetries included the cortical thickness of Rolandic regions and the volume of thalami. Results Fatigue symptoms increased together with the functional inter-hemispheric imbalance of sensorimotor homologous areas activities at rest and during movement, in absence of any appreciable parenchymal asymmetries. Conclusion People with MS suffering of chronic fatigue have higher left (dominant) than right hemispheric primary sensorimotor activity power in the resting state, without structural asymmetries. Key message This finding supports the development of compensative interventions that may revert these neuronal activity imbalances to relieve fatigue in MS.

Purpose: Transcranial electric stimulations (tES) with amplitude-modulated currents are promising tools to enhance neuromodulation effects. It is essential to select the correct cortical targets and inhibitory/excitatory protocols to reverse changes in specific networks. We aimed at assessing the dependence of cortical excitability changes on the current amplitude of 20 Hz transcranial alternating current stimulation (tACS) over the bilateral primary motor cortex. Methods: We chose two amplitude ranges of the stimulations, around 25 ?A/cm2 and 63 ?A/cm2 from peak to peak, with three values (at steps of about 2.5%) around each, to generate, respectively, inhibitory and excitatory effects of the primary motor cortex. We checked such changes online through transcranial magnetic stimulation (TMS)-induced motor evoked potentials (MEPs). Results: Cortical excitability changes depended upon current density (p = 0.001). Low current densities decreased MEP amplitudes (inhibition) while high current densities increased them (excitation). Conclusions: tACS targeting bilateral homologous cortical areas can induce online inhibition or excitation as a function of the current density.

Selecting an optimal event distribution for experimental use in event-related fMRI studies can require the generation of large numbers of event sequences with characteristics hard to control. The use of known probability distributions offers the possibility to control event timing and constrain the search space for finding optimal event sequences. We investigated different probability distributions in terms of response estimation (estimation efficiency), detectability (detection power, parameter estimation efficiency, sensitivity to true positives), and false-positive activation. Numerous simulated event sequences were generated selecting interevent intervals (IEI) from the uniform, uniform permuted, Latin square, exponential, binomial, Poisson, chi(2), geometric, and bimodal probability distributions and fixed IEI. Event sequences from the bimodal distribution, like block designs, had the best performance for detection and the poorest for estimation, while high estimation and detectability occurred for the long-decay exponential distribution. The uniform distribution also yielded high estimation efficiency, but probability functions with a long tail toward higher IEI, such as the geometric and the chi(2) distributions, had superior detectability. The distributions with the best detection performance also had a relatively high incidence of false positives, in contrast to the ordered distributions (Latin square and uniform permuted). The predictions of improved sensitivities for distributions with long tails were confirmed with empirical data. Moreover, the Latin square design yielded detection of activated voxels similar to the chi(2) distribution. These results indicate that high detection and suitable behavioral designs have compatibility for application of functional MRI methods to experiments requiring complex designs.

Purpose This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called LABS: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction. Methods This method was first tested on morphological T1-weighted MRIs of 30 healthy subjects. Its reliability was further confirmed by including neurological patients (with Alzheimer's Disease) from the ADNI repository, in whom the presence of volumetric loss within the brainstem had been previously described. Segmentation accuracies were evaluated against expert-drawn manual delineation. To evaluate the quality of LABS segmentation we used volumetric, spatial overlap and distance-based metrics. Results The comparison between the quantitative measurements provided by LABS against manual segmentations revealed excellent results in healthy controls when considering either the midbrain (DICE measures higher that 0.9; Volume ratio around 1 and Hausdorff distance around 3) or the pons (DICE measures around 0.93; Volume ratio ranging 1.024–1.05 and Hausdorff distance around 2). Similar performances were detected for AD patients considering segmentation of the pons (DICE measures higher that 0.93; Volume ratio ranging from 0.97–0.98 and Hausdorff distance ranging 1.07–1.33), while LABS performed lower for the midbrain (DICE measures ranging 0.86–0.88; Volume ratio around 0.95 and Hausdorff distance ranging 1.71–2.15). Conclusions Our study represents the first attempt to validate a new fully automated method for in vivo segmentation of two anatomically complex brainstem subregions. We retain that our method might represent a useful tool for future applications in clinical practice.

Multiple sclerosis (MS) affects myelin sheaths within the central nervous system, concurring to cause brain atrophy and neurodegeneration as well as gradual functional disconnections. To explore early signs of altered connectivity in MS from a structural and functional perspective, the morphology of corpus callosum (CC) was correlated with a dynamic inter-hemispheric connectivity index.Twenty mildly disabled patients affected by a relapsing-remitting (RR) form of MS (EDSS.

Multiple sclerosis-related fatigue is highly common and often refractory to medical therapy. Ten fatigued multiple sclerosis patients received two blocks of 5-day anodal bilateral primary somatosensory areas transcranial direct current stimulation in a randomized, doubleblind sham-controlled, cross-over study. The real neuromodulation by a personalized electrode, shaped on the MRderived primary somatosensory cortical strip, reduced fatigue in all patients, by 26% in average ( p = 0.002), which did not change after sham ( p = 0.901). Anodal tDCS over bilateral somatosensory areas was able to relief fatigue in mildly disabled MS patients, when the fatigue related symptoms severely hamper their quality of life. These small-scale study results support the concept that interventions modifying the sensorimotor network activity balances could be a suitable non-pharmacological treatment for multiple sclerosis fatigue.

Background: Highly common in multiple sclerosis (MS), fatigue severely impacts patients’ daily lives. Previous findings of altered connectivity patterns led to the hypothesis that the distortion of functional connections within the brain-muscle circuit plays a crucial pathogenic role. Objective: The objective of this paper is to identify markers sensitive to fatigue in multiple sclerosis. Methods: Structural (magnetic resonance imaging with assessment of thalamic volume and cortical thickness of the primary sensorimotor areas) and functional (cortico-muscular coherence (CMC) from simultaneous electroencephalo- and surface electromyographic recordings during a weak handgrip task) measures were used on 20 mildly disabled MS patients (relapsing–remitting course, Expanded Disability Status Scale score ≤ 2) who were recruited in two fatigue-dependent groups according to the Modified Fatigue Index Scale (MFIS) score. Results: The two groups were similar in terms of demographic, clinical and imaging features, as well as task execution accuracy and weariness. In the absence of any fatigue-dependent brain and muscular oscillatory activity alterations, CMC worked at higher frequencies as fatigue increased, explaining 67% of MFIS variance ( p=.002). Conclusion: Brain-muscle functional connectivity emerged as a sensitive marker of phenomena related to the origin of MS fatigue, impacting central-peripheral communication well before the appearance of any impairment in the communicating nodes.

We present a new application based on genetic algorithms (GAs) that evolves a Cellular Neural Network (CNN) capable of automatically determining the lesion load in multiple sclerosis (MS) patients from magnetic resonance imaging (MRI). In particular, it seeks to identify brain areas affected by lesions, whose presence is revealed by areas of higher intensity if compared to healthy tissue. The performance of the CNN algorithm has been quantitatively evaluated by comparing the CNN output with the expert's manual delineation of MS lesions. The CNN algorithm was run on a data set of 11 MS patients; for each one a single dataset of MRI images (matrix resolution of 256 × 256 pixels) was acquired. Our automated approach gives satisfactory results showing that after the learning process the CNN is capable of detecting MS lesions with different shapes and intensities (mean DICE coefficient = 0.64). The system could provide a useful support tool for the evaluation of lesions in MS patients, although it needs to be evolved and developed in the future. © 2011 Elsevier B.V.

Demyelination and axonal damage are pathologic hallmarks of multiple sclerosis (MS), leading to loss of neuronal synchronization, functional disconnection amongst brain relays, and clinical sequelae. To investigate these properties, the primary component of the sensorimotor network was analyzed in mildly disabled Relapsing-Remitting MS patients without sensory symptoms at the time of the investigation. By magnetoencephalography (MEG), the recruitment pattern within the primary sensory (S1) and motor (M1) areas was estimated through the morphology of the early components of somatosensory evoked magnetic fields (SEFs), after evaluating the S1 responsiveness to sensory inputs from the contralateral arm. In each hemisphere, network recruitment properties were correlated with ispilateral thalamus volume, estimated by morphometric techniques upon high-resolution 3D structural magnetic resonance images (MRI). S1 activation was preserved, whereas SEF morphology was strikingly distorted in MS patients, marking a disruption of primary somatosensory network patterning. An unbalance of S1–M1 dynamic recruitment was documented and correlated with the thalamic volume reduction in the left hemisphere. These findings support the model of MS as a disconnection syndrome, with major susceptibility to damage experienced by nodes belonging to more frequently recruited and highly specialized networks. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc.

Alzheimer's disease (AD) is a heterogeneous and progressive neurodegenerative disorder. The recent Metal Hypothesis states that the interaction of Amyloid beta (Abeta, the main constituent of senile plaques) with transition metals is at the basis of AD neurodegeneration. This hypothesis is based on in vitro studies demonstrating that metals (copper, zinc) accelerate the aggregation and precipitation into plaques of Abeta, ultimately leading to synaptic dysfunction and accelerated amyloidogenesis. Recently, we have identified in AD patients a specific 'copper disease' marker, consisting in a serum-increase of copper not bound to ceruloplasmin, named 'free' copper. Several patents have been issued in the recent years and many clinical trials have been attempted in search of an anti-metal effect counteracting AD progression. Some of them have delivered very encouraging results. These anti-metal agents, however, have also shown adverse events. This work is aimed at reviewing 'old' and 'new' attitudes towards the use of anti-copper complexing agents or biological molecules which induce or maintain a state of copper malabsorption, such as zinc compounds, paying special attention to how such a rethinking of 'old' clinical trials might trace new routes in planning 'modern' ones.

Relatively little is known about genetic determinants of cognitive dysfunction in multiple sclerosis (MS). A growing body of evidence demonstrates that a functional variant of the brain-derived neurotrophic factor (BDNF) gene, the Val 66 Met polymorphism, contributes to poor hippocampal and prefrontal functions, particularly memory processes, in healthy controls. In contrast, findings from previous association studies examining this polymorphism and memory performance in MS patients yielded conflicting results. However, the way in which this BDNF polymorphism affects brain function in MS patients has not been examined. In line with the “intermediate phenotype” approach, we assessed effects of the BDNF Val 66 Met polymorphism on brain activity during a spatial working memory task. We used functional magnetic resonance imaging (fMRI) to measure brain responses in a total of 61 subjects comprising 29 relapsing–remitting MS patients and 32 healthy controls. The fMRI results demonstrated association of the BDNF polymorphism with brain activity during working memory, with opposite effects in MS patients and controls. Healthy carriers of the Met 66 allele showed increased activation of the parieto-prefrontal network and altered disengagement of the ventro-medial prefrontal cortex and hippocampus in comparison with their respective Val 66 counterparts. Analysis within the group demonstrated that this working memory-related activation pattern was absent in MS patients. Our imaging genetic study demonstrates that the Val 66 Met polymorphism of the BDNF gene contributes to some of the individual variability in the functional response to a working memory challenge in healthy controls but it does not provide evidence for an MS-specific pattern of gene action.

Neuroimaging findings, including repetitive transcranial magnetic stimulation (rTMS) interference, point to an engagement of prefrontal cortex (PFC) in learning and memory. Whether parietal cortex (PC) activity is causally linked to successful episodic encoding and retrieval is still uncertain. We compared the effects of event-related active or sham rTMS (a rapid-rate train coincident to the very first phases of memoranda presentation) to the left or right intraparietal sulcus, during a standardized episodic memory task of visual scenes, with those obtained in a fully matched sample of subjects who received rTMS on left or right dorsolateral PFC during the same task. In these subjects, specific hemispheric effects of rTMS included interference with encoding after left stimulation and disruption of retrieval after right stimulation. The interference of PC-rTMS on encoding/retrieval performance was negligible, lacking specificity even when higher intensities of stimulation were applied. However, right PC-rTMS of the same intensity lengthened reaction times in the context of a purely attentive visuospatial task. These results suggest that the activity of intraparietal sulci shown in several functional magnetic resonance studies on memory, unlike that of the dorsolateral PFC, is not causally engaged to a useful degree in memory encoding and retrieval of visual scenes. The parietal activations accompanying the memorization processes could reflect the engagement of a widespread brain attentional network, in which interference on a single 'node' is insufficient for an overt disruption of memory performance.

Functional magnetic resonance imaging and transcranial magnetic stimulation were used to examine a 34 year-old right-handed patient, who, at the age of 6 years, had experienced sudden right hemiplegia, seizures, and stupor during a bout of measles encephalitis, followed by incomplete distal right motor recovery. Morphological MRI showed massive unilateral enlargement of the left ventricle, associated with extreme thinning of the white and gray matter, with partial preservation of the pyramidal tract. Functional MRI and transcranial magnetic stimulation revealed reorganization of the motor cortices, and integrity of the corticospinal pathway, respectively. Our findings indicate that complete hand motor recovery may require functional connections between the motor cortical areas and cortical-subcortical structures, in addition to the retained integrity of the primary sensorimotor area and pyramidal tract.