Abnormal frontoparietal synaptic gain mediating the P300 in patients with psychotic disorder and their unaffected relatives.

The "dysconnection hypothesis" of psychosis suggests that a disruption of functional integration underlies cognitive deficits and clinical symptoms. Impairments in the P300 potential are well documented in psychosis. Intrinsic (self-)connectivity in a frontoparietal cortical hierarchy during a P300 experiment was investigated. Dynamic Causal Modeling was used to estimate how evoked activity results from the dynamics of coupled neural populations and how neural coupling changes with the experimental factors. Twenty-four patients with psychotic disorder, twenty-four unaffected relatives, and twenty-five controls underwent EEG recordings during an auditory oddball paradigm. Sixteen frontoparietal network models (including primary auditory, superior parietal, and superior frontal sources) were analyzed and an optimal model of neural coupling, explaining diagnosis and genetic risk effects, as well as their interactions with task condition were identified. The winning model included changes in connectivity at all three hierarchical levels. Patients showed decreased self-inhibition-that is, increased cortical excitability-in left superior frontal gyrus across task conditions, compared with unaffected participants. Relatives had similar increases in excitability in left superior frontal and right superior parietal sources, and a reversal of the normal synaptic gain changes in response to targets relative to standard tones. It was confirmed that both subjects with psychotic disorder and their relatives show a context-independent loss of synaptic gain control at the highest hierarchy levels. The relatives also showed abnormal gain modulation responses to task-relevant stimuli. These may be caused by NMDA-receptor and/or GABAergic pathologies that change the excitability of superficial pyramidal cells and may be a potential biological marker for psychosis. Hum Brain Mapp 38:3262-3276, 2017. © 2017 Wiley Periodicals, Inc.

Comparison of the Latin America Regulation Landscape and International Reference Health Authorities to Hasten Drug Registration and Clinical Research Applications.

INTRODUCTION: Promptly providing new drugs to fulfill unmet medical needs requires changes in drug development and registration processes. Health Authorities (HAs) considered as reference due to their experience and acknowledgement (Food and Drug Administration [FDA] among others) already consider innovative clinical trial (CT) designs and flexible approval procedures, but Latin America (LATAM) regulations are still far. A comparison was performed to identify gaps. MATERIALS AND METHODS: CT requirements for drug Marketing Authorization Application (MAA) and CT approval regulations were compared between LATAM and reference HAs (FDA/European Medicines Agency [EMA]/Health-Canada/Swissmedic/Therapeutic Goods Administration [TGA]/Pharmaceuticals and Medical Devices Agency [PMDA]), as of August 2022. Procedure included reference HAs regulations review, item selection, identification in LATAM regulations, and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines (ICH-E6[R2]/ICH-E8[R1]) implementation revision. RESULTS: For MAA, specific application requirements or ICH guideline M4(R4) on common technical document (CTD) adoption are generally stated, and phase-I/III performance is mandatory (explicitly/implicitly). Faster patient access procedures are infrequent: Priority-drug programs, conditional authorizations, or expedited procedures are scarce or non-existent. Regulatory reliance procedures are adopted through different pathways. Regarding CT approval, innovative/complex CT designs are not prohibited but usually omitted. Some countries implemented adapted CT conducting during the COVID-19 pandemic. Early scientific advice meetings (HA-sponsor) are occasionally considered. Most countries are not formally ICH-joined. CONCLUSIONS: LATAM regulations must adapt to new regulatory standards (FDA/EMA/ICH) through implementation of frequent updates, reliance/expedited procedures, early HA-sponsor interactions, innovative/complex CTs, mandatory phase-III reaching elimination, and decentralized elements for CT conducting.

Neuropsychological networks in cognitively healthy older adults and dementia patients.

Neuropsychological tests have commonly been used to determine the organization of cognitive functions by identifying latent variables. In contrast, an approach which has seldom been employed is network analysis. We characterize the network structure of a set of representative neuropsychological test scores in cognitively healthy older adults and MCI and dementia patients using network analysis. We employed the neuropsychological battery from the National Alzheimer's Coordinating Center which included healthy controls (n = 7623), mild cognitive impairment patients (n = 5981) and dementia patients (n = 2040), defined according to the Clinical Dementia Rating. The results showed that, according to several network analysis measures, the most central cognitive function is executive function followed by attention, language, and memory. At the test level, the most central test was the Trail Making Test B, which measures cognitive flexibility. Importantly, these results and most other network measures, such as the community organization and graph representation, were similar across the three diagnostic groups. Therefore, network analysis can help to establish a ranking of cognitive functions and tests based on network centrality and suggests that this organization is preserved in dementia. Central nodes might be particularly relevant both from a theoretical and clinical point of view, as they are more associated with other nodes, and their disruption is likely to have a larger effect on the overall network than peripheral nodes. The present analysis may provide a proof of principle for the application of network analysis to cognitive data.

Preserved semantic categorical organization in mild cognitive impairment: A network analysis of verbal fluency.

The decline in semantic verbal fluency as we age may originate from both semantic memory degradation and executive function deficits. We investigated to what extent semantic memory is organized into categories in older adults with mild cognitive impairment (n = 81) and healthy controls (n = 83). We obtained the semantic networks automatically from the probability of co-occurrence of words in a verbal fluency test and characterized them with graph-theory tools. We found that the degree of categorical organization was similar for both diagnostic groups, but there was a higher tendency to transition to other categories during word production in the patient group. These results suggest that the semantic network is preserved in mild cognitive impairment, but also that the existing associations are exploited less efficiently during long-term memory search, possibly because of deficits in executive function.

Detection of early stages of Alzheimer's disease based on MEG activity with a randomized convolutional neural network.

The early detection of Alzheimer's disease can potentially make eventual treatments more effective. This work presents a deep learning model to detect early symptoms of Alzheimer's disease using synchronization measures obtained with magnetoencephalography. The proposed model is a novel deep learning architecture based on an ensemble of randomized blocks formed by a sequence of 2D-convolutional, batch-normalization and pooling layers. An important challenge is to avoid overfitting, as the number of features is very high (25755) compared to the number of samples (132 patients). To address this issue the model uses an ensemble of identical sub-models all sharing weights, with a final stage that performs an average across sub-models. To facilitate the exploration of the feature space, each sub-model receives a random permutation of features. The features correspond to magnetic signals reflecting neural activity and are arranged in a matrix structure interpreted as a 2D image that is processed by 2D convolutional networks. The proposed detection model is a binary classifier (disease/non-disease), which compared to other deep learning architectures and classic machine learning classifiers, such as random forest and support vector machine, obtains the best classification performance results with an average F1-score of 0.92. To perform the comparison a strict validation procedure is proposed, and a thorough study of results is provided.

Can we observe collective neuronal activity from macroscopic aggregate signals?

The fundamental problem faced by noninvasive neuroimaging techniques such as EEG/MEG(1) is to elucidate functionally important aspects of the microscopic neuronal network dynamics from macroscopic aggregate measurements. Due to the mixing of the activities of large neuronal populations in the observed macroscopic aggregate, recovering the underlying network that generates the signal in the absence of any additional information represents a considerable challenge. Recent MEG studies have shown that macroscopic measurements contain sufficient information to allow the differentiation between patterns of activity, which are likely to represent different stimulus-specific collective modes in the underlying network (Hadjipapas, A., Adjamian, P., Swettenham, J.B., Holliday, I.E., Barnes, G.R., 2007. Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex. NeuroImage 35, 518-530). The next question arising in this context is whether aspects of collective network activity can be recovered from a macroscopic aggregate signal. We propose that this issue is most appropriately addressed if MEG/EEG signals are to be viewed as macroscopic aggregates arising from networks of coupled systems as opposed to aggregates across a mass of largely independent neural systems. We show that collective modes arising in a network of simulated coupled systems can be indeed recovered from the macroscopic aggregate. Moreover, we show that nonlinear state space methods yield a good approximation of the number of effective degrees of freedom in the network. Importantly, information about hidden variables, which do not directly contribute to the aggregate signal, can also be recovered. Finally, this theoretical framework can be applied to experimental MEG/EEG data in the future, enabling the inference of state dependent changes in the degree of local synchrony in the underlying network.

Brain imaging of acupuncture: comparing superficial with deep needling.

The difference between superficial and deep needling at acupuncture points has yet to be mapped with functional magnetic resonance imaging (fMRI). Using a 3T MRI, echo planar imaging data were acquired for 17 right-handed healthy volunteer participants. Two fMRI scans of acupuncture needling were taken in random order in a block design, one for superficial and one for deep needling on the right hand at the acupuncture point LI-4 (Hegu), with the participant blind to the order. For both scans needle stimulation was used. Brain image analysis tools were used to explore within-group and between-group differences in the blood oxygen level dependent (BOLD) responses. The study demonstrated marked similarities in BOLD signal responses between superficial and deep needling, with no significant differences in either activations (increases in BOLD signal) or deactivations (decreases in BOLD signal) above the voxel Z score of 2.3 with corrected cluster significance of P=0.05. For both types of needling, deactivations predominated over activations. These fMRI data suggest that acupuncture needle stimulation at two different depths of needling, superficial and deep, do not elicit significantly different BOLD responses. This data is consistent with the equivalent therapeutic outcomes that are claimed by proponents of Japanese and Chinese styles of acupuncture that utilise superficial and deep needling, respectively.

An efficient implementation of the synchronization likelihood algorithm for functional connectivity.

Measures of functional connectivity are commonly employed in neuroimaging research. Among the most popular measures is the Synchronization Likelihood which provides a non-linear estimate of the statistical dependencies between the activity time courses of different brain areas. One aspect which has limited a wider use of this algorithm is the fact that it is very computationally and memory demanding. In the present work we propose new implementations and parallelizations of the Synchronization Likelihood algorithm with significantly better performance both in time and in memory use. As a result both the amount of required computational time is reduced by 3 orders of magnitude and the amount of memory needed for calculations is reduced by 2 orders of magnitude. This allows performing analyses that were not feasible before from a computational standpoint.

Effects on orientation perception of manipulating the spatio-temporal prior probability of stimuli.

Spatial and temporal regularities commonly exist in natural visual scenes. The knowledge of the probability structure of these regularities is likely to be informative for an efficient visual system. Here we explored how manipulating the spatio-temporal prior probability of stimuli affects human orientation perception. Stimulus sequences comprised four collinear bars (predictors) which appeared successively towards the foveal region, followed by a target bar with the same or different orientation. Subjects' orientation perception of the foveal target was biased towards the orientation of the predictors when presented in a highly ordered and predictable sequence. The discrimination thresholds were significantly elevated in proportion to increasing prior probabilities of the predictors. Breaking this sequence, by randomising presentation order or presentation duration, decreased the thresholds. These psychophysical observations are consistent with a Bayesian model, suggesting that a predictable spatio-temporal stimulus structure and an increased probability of collinear trials are associated with the increasing prior expectation of collinear events. Our results suggest that statistical spatio-temporal stimulus regularities are effectively integrated by human visual cortex over a range of spatial and temporal positions, thereby systematically affecting perception.

Signal-to-noise ratio of the MEG signal after preprocessing.

BACKGROUND: Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise. NEW METHOD: The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials. RESULTS: Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5-10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI. COMPARISON WITH EXISTING METHODS: No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed. CONCLUSIONS: The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.

Reduction of pain thresholds in fibromyalgia after very low-intensity magnetic stimulation: a double-blinded, randomized placebo-controlled clinical trial.

BACKGROUND: Exposure to electromagnetic fields has been reported to have analgesic and antinociceptive effects in several organisms. OBJECTIVE: To test the effect of very low-intensity transcranial magnetic stimulation on symptoms associated with fibromyalgia syndrome. METHODS: A double-blinded, placebo-controlled clinical trial was performed in the Sagrado Corazón Hospital, Seville, Spain. Female fibromyalgia patients (22 to 50 years of age) were randomly assigned to either a stimulation group or a sham group. The stimulation group (n=28) was stimulated using 8 Hz pulsed magnetic fields of very low intensity, while the sham group (n=26) underwent the same protocol without stimulation. Pressure pain thresholds before and after stimulation were determined using an algometer during the eight consecutive weekly sessions of the trial. In addition, blood serotonin levels were measured and patients completed questionnaires to monitor symptom evolution. RESULTS: A repeated-measures ANOVA indicated statistically significant improvement in the stimulation group compared with the control group with respect to somatosensory pain thresholds, ability to perform daily activities, perceived chronic pain and sleep quality. While improvement in pain thresholds was apparent after the first stimulation session, improvement in the other three measures occurred after the sixth week. No significant between-group differences were observed in scores of depression, fatigue, severity of headaches or serotonin levels. No adverse side effects were reported in any of the patients. CONCLUSIONS: Very low-intensity magnetic stimulation may represent a safe and effective treatment for chronic pain and other symptoms associated with fibromyalgia.

Increased brain responses during subjectively-matched mechanical pain stimulation in fibromyalgia patients as evidenced by MEG.

OBJECTIVE: The precise pathophysiology of fibromyalgia, a syndrome characterized by chronic widespread pain, remains to be clarified. When subjected to the same amount of stimulation, patients show enhanced brain responses as compared to controls, providing evidence of central pain augmentation in this syndrome. We aimed to characterize brain response differences when stimulation is adjusted to elicit similar subjective levels of pain in both groups. METHODS: Magnetoencephalography (MEG) was used to investigate the brain responses to pressure stimulation applied both above and below the pain threshold in nine patients and nine control subjects. A device was developed to deliver pressure pulses in a quantifiable and precise manner. The amount of pressure was adjusted to produce similar subjective pain in both groups. RESULTS: A between-group comparison of differences between responses evoked by stimulation above and below the pain threshold was performed using cluster-based permutation testing. Increases in signal amplitude in somatosensory, temporal and parietal areas at short latencies, and in prefrontal areas at both short and long latencies, were found to be larger for patients than for control subjects. CONCLUSION: Fibromyalgia patients show enhanced brain responses after reducing the amount of pressure to produce similar subjective levels of pain than to the control subjects. SIGNIFICANCE: The present results suggest that central pain augmentation is present in fibromyalgia, not only when the objective level of stimulation is kept the same as for control subjects, but also when stimulation is adjusted to produce similar levels of pain in patients and controls.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required.

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50-100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Brain activity patterns in stable and progressive mild cognitive impairment during working memory as evidenced by magnetoencephalography.

It has been reported that mild cognitive impairment (MCI) patients, when compared with controls, show increased activity in different brain regions within the ventral pathway during memory tasks. A key question is whether this profile of increased activity could be useful to predict which patients will develop dementia. Herein, we present profiles of brain magnetic activity during a memory task recorded with magnetoencephalography from MCI patients (N = 10), Alzheimer's disease (AD) patients (N = 10), and healthy volunteers (N = 17). After 2½ years of follow-up, five of the MCI patients developed AD. Patients who progressed to AD (PMCI) showed higher activity than those who remained stable (SMCI), AD patients and controls. This increased activity in PMCI patients involves regions within the ventral and dorsal pathways. In contrast, SMCI patients showed higher activation than controls only along the ventral pathway. This increase in both the ventral and dorsal pathways in PMCI patients may reflect a compensatory mechanism for the loss in efficiency in memory networks, which would be absent in AD patients as they showed lower activity levels than the rest of the groups.

Increased biomagnetic activity in healthy elderly with subjective memory complaints.

OBJECTIVE: Subjective memory complaints (SMCs) are frequently reported by elderly people with or without objective cognitive impairment (OMI) as assessed by neuropsychological tests. We investigate whether SMCs are associated with altered brain biomagnetic patterns even in the absence of OMI. METHODS: We report spatio-temporal patterns of brain magnetic activity recorded with magnetoencephalography during a memory task in 51 elderly participants divided into the following groups: patients with mild cognitive impairment (MCI) with SMC and OMI, individuals with SMC but not OMI, and healthy controls without neither SMC nor OMI. Exclusion criteria for all three groups included a diagnosis of depression or any other psychiatric condition. RESULTS: No statistically significant differences were found between MCI patients and participants with SMC. However, the SMC showed higher activation, between 200 and 900 ms after stimulus onset, than the control group in posterior ventral regions and in the dorsal pathway. MCI patients showed higher activation than the control group in the posterior part of the ventral pathway. CONCLUSIONS: These findings suggest that similar physiological mechanisms may underlie SMC and MCI, which could be two stages in a cognitive continuum. SIGNIFICANCE: MEG provide different neurophysiological profiles between SMC and control subjects.

Reorganization of functional networks in mild cognitive impairment.

Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD.

Functional connectivity in mild cognitive impairment during a memory task: implications for the disconnection hypothesis.

Mild cognitive impairment (MCI) has been considered an intermediate state between healthy aging and dementia. The early damage in anatomical connectivity and progressive loss of synapses that characterize early Alzheimer's disease suggest that MCI could also be a disconnection syndrome. Here, we compare the degree of synchronization of brain signals recorded with magnetoencephalography from patients (22) with MCI with that of healthy controls (19) during a memory task. Synchronization Likelihood, an index based on the theory of nonlinear dynamical systems, was used to measure functional connectivity. During the memory task patients showed higher interhemispheric synchronization than healthy controls between left and right -anterior temporo-frontal regions (in all studied frequency bands) and in posterior regions in the γ band. On the other hand, the connectivity pattern from healthy controls indicated two clusters of higher synchronization, one among left temporal sensors and another one among central channels. Both of them were found in all frequency bands. In the γ band, controls showed higher Synchronization Likelihood values than MCI patients between central-posterior and frontal-posterior channels and a high synchronization in posterior regions. The inter-hemispheric increased synchronization values could reflect a compensatory mechanism for the lack of efficiency of the memory networks in MCI patients. Therefore, these connectivity profiles support only partially the idea of MCI as a disconnection syndrome, as patients showed increased long distance inter-hemispheric connections but a decrease in antero-posterior functional connectivity.

Spatio-temporal prediction and inference by V1 neurons.

In normal vision, visual scenes are predictable, as they are both spatially and temporally redundant. Evidence suggests that the visual system may use the spatio-temporal regularities of the external world, available in the retinal signal, to extract information from the visual environment and better reconstruct current and future stimuli. We studied this by recording neuronal responses of primary visual cortex (area V1) in anaesthetized and paralysed macaques during the presentation of dynamic sequences of bars, in which spatio-temporal regularities and local information were independently manipulated. Most V1 neurons were significantly modulated by events prior to and distant from stimulation of their classical receptive fields (CRFs); many were more strongly tuned to prior and distant events than they were to CRFs bars; and several showed tuning to prior information without any CRF stimulation. Hence, V1 neurons do not simply analyse local contours, but impute local features to the visual world, on the basis of prior knowledge of a visual world in which useful information can be distributed widely in space and time.

Functional imaging and neural information coding.

Measuring functional magnetic resonance imaging (fMRI) responses to parametric stimulus variations in imaging experiments can elucidate how sensory information is represented in the brain. However, a potential limitation of this approach is that fMRI responses reflect only a regional average of neuronal activity. For this reason stimulus-induced changes in fMRI signal may not always reflect how sensory information is encoded by neuronal population activity. We investigate the potential problems induced by the finite spatial resolution of the fMRI signal by combining the principles of Information Theory with a computational model of neuronal activity based on known tuning properties of sensory cortex and assuming a linear spike rate to fMRI signal relationship. We found that the relationship between neuronal information and fMRI signal is highly nonlinear. It follows that the brain voxel experiencing the largest fMRI signal change is not necessarily the voxel encoding the most sensory information. Results also show that functional imaging data can be better interpreted in terms of neural information processing if the fMRI data and some knowledge about the tuning properties of the underlying neuronal populations are incorporated into a computational model. We discuss how imaging techniques themselves may provide an estimation of neuronal tuning properties.