Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders

Using large-scale neural models to interpret connectivity measures of cortico-cortical dynamics at millisecond temporal resolution

Over the last two decades numerous functional imaging studies have shown that higher order cognitive functions are crucially dependent on the formation of distributed, large-scale neuronal assemblies (neurocognitive networks), often for very short durations. This has fueled the development of a vast number of functional connectivity measures that attempt to capture the spatiotemporal evolution of neurocognitive networks. Unfortunately, interpreting the neural basis of goal directed behavior using connectivity measures on neuroimaging data are highly dependent on the assumptions underlying the development of the measure, the nature of the task, and the modality of the neuroimaging technique that was used. This paper has two main purposes. The first is to provide an overview of some of the different measures of functional/effective connectivity that deal with high temporal resolution neuroimaging data. We will include some results that come from a recent approach that we have developed to identify the formation and extinction of task-specific, large-scale neuronal assemblies from electrophysiological recordings at a ms-by-ms temporal resolution. The second purpose of this paper is to indicate how to partially validate the interpretations drawn from this (or any other) connectivity technique by using simulated data from large-scale, neurobiologically realistic models. Specifically, we applied our recently developed method to realistic simulations of MEG data during a delayed match-to-sample (DMS) task condition and a passive viewing of stimuli condition using a large-scale neural model of the ventral visual processing pathway. Simulated MEG data using simple head models were generated from sources placed in V1, V4, IT, and prefrontal cortex (PFC) for the passive viewing condition. The results show how closely the conclusions obtained from the functional connectivity method match with what actually occurred at the neuronal network level.

EEG synchronization characteristics of functional connectivity and complex network properties of memory maintenance in the delta and theta frequency bands

Task-dependent changes of nonlinear-linear synchronization features and graph theoretical properties of the delta and theta frequencies were analyzed in the present EEG study that were related to episodic memory maintenance processes. Synchronization was found to increase with respect to both the delta and theta bands within the frontal and parietal areas and also between these regions. Results of graph theoretical analysis indicated a task-related shift towards small-world network topology in the theta band.

Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease

The relation between pathology and cognitive dysfunction in dementia is still poorly understood, although disturbed communication between different brain regions is almost certainly involved. In this study we combine magneto-encephalography (MEG) and network analysis to investigate the role of functional sub-networks (modules) in the brain with regard to cognitive failure in Alzheimer's disease. Whole-head resting-state (MEG) was performed in 18 Alzheimer patients (age 67 ± 9, 6 females, MMSE 23 ± 5) and 18 healthy controls (age 66 ± 9, 11 females, MMSE 29 ± 1). We constructed functional brain networks based on interregional synchronization measurements, and performed graph theoretical analysis with a focus on modular organization. The overall modular strength and the number of modules changed significantly in Alzheimer patients. The parietal cortex was the most highly connected network area, but showed the strongest intramodular losses. Nonetheless, weakening of intermodular connectivity was even more outspoken, and more strongly related to cognitive impairment. The results of this study demonstrate that particularly the loss of communication between different functional brain regions reflects cognitive decline in Alzheimer's disease. These findings imply the relevance of regarding dementia as a functional network disorder.

Large-scale functional brain networks in human non-rapid eye movement sleep: insights from combined electroencephalographic/functional magnetic resonance imaging studies

This paper reviews the existing body of knowledge on the neural correlates of spontaneous oscillations, functional connectivity and brain plasticity in human non-rapid eye movement (NREM) sleep. The first section reviews the evidence that specific sleep events as slow waves and spindles are associated with transient increases in regional brain activity. The second section describes the changes in functional connectivity during NREM sleep, with a particular focus on changes within a low-frequency, large-scale functional brain network. The third section will discuss the possibility that spontaneous oscillations and differential functional connectivity are related to brain plasticity and systems consolidation, with a particular focus on motor skill acquisition. Implications for the mode of information processing per sleep stage and future experimental studies are discussed.

Milestones in clinical neurophysiology

Over the last 25 years, clinical neurophysiology has made many advances in the understanding, diagnosis, and even treatment of different movement disorders. Transcranial magnetic stimulation has been the biggest technical advance. Progress in pathophysiology includes improved knowledge about bradykinesia in Parkinson's disease, loss of inhibition and increased plasticity in dystonia, abnormal startle in hyperekplexia, and various features of psychogenic movement disorders that can aid diagnosis. Studies have been done looking at the use of noninvasive brain stimulation for therapy, but effects are generally small.

TrkB signaling in parvalbumin-positive interneurons is critical for gamma-band network synchronization in hippocampus

Although brain-derived neurotrophic factor (BDNF) is known to regulate circuit development and synaptic plasticity, its exact role in neuronal network activity remains elusive. Using mutant mice (TrkB-PV(-/-)) in which the gene for the BDNF receptor, tyrosine kinase B receptor (trkB), has been specifically deleted in parvalbumin-expressing, fast-spiking GABAergic (PV+) interneurons, we show that TrkB is structurally and functionally important for the integrity of the hippocampal network. The amplitude of glutamatergic inputs to PV+ interneurons and the frequency of GABAergic inputs to excitatory pyramidal cells were reduced in the TrkB-PV(-/-) mice. Functionally, rhythmic network activity in the gamma-frequency band (30-80 Hz) was significantly decreased in hippocampal area CA1. This decrease was caused by a desynchronization and overall reduction in frequency of action potentials generated in PV+ interneurons of TrkB-PV(-/-) mice. Our results show that the integration of PV+ interneurons into the hippocampal microcircuit is impaired in TrkB-PV(-/-) mice, resulting in decreased rhythmic network activity in the gamma-frequency band.

Functional connectivity changes in multiple sclerosis patients: a graph analytical study of MEG resting state data

Multiple sclerosis (MS) is characterized by extensive damage in the central nervous system. Within this field, there is a strong need for more advanced, functional imaging measures, as abnormalities measured with structural imaging insufficiently explain clinicocognitive decline in MS. In this study we investigated functional connectivity changes in MS using resting-state magnetoencephalography (MEG). Data from 34 MS patients and 28 age and gender-matched controls was assessed using synchronization likelihood (SL) as a measure of functional interaction strength between brain regions, and graph analysis to characterize topological patterns of connectivity changes. Cognition was assessed using extensive neuropsychological evaluation. Structural measures included brain and lesion volumes, using MRI. Results show SL increases in MS patients in theta, lower alpha and beta bands, with decreases in the upper alpha band. Graph analysis revealed a more regular topology in the lower alpha band in patients, indicated by an increased path length (λ) and clustering coefficient (γ). Attention and working memory domains were impaired, with decreased brain volumes. A stepwise linear regression model using clinical, MRI and MEG parameters as predictors revealed that only increases in lower alpha band γ predicted impaired cognition. Cognitive impairments and related altered connectivity patterns were found to be especially predominant in male patients. These results show specific functional changes in MS as measured with MEG. Only changes in network topology were related to poorer cognitive outcome. This indicates the value of graph analysis beyond traditional structural and functional measures, with possible implications for diagnostic and/or prognostic purposes in MS.

Dynamics of dynamics within a single data acquisition session: variation in neocortical alpha oscillations in human MEG

BACKGROUND

Behavioral paradigms applied during human recordings in electro- and magneto- encephalography (EEG and MEG) typically require 1-2 hours of data collection. Over this time scale, the natural fluctuations in brain state or rapid learning effects could impact measured signals, but are seldom analyzed.

METHODS AND FINDINGS

We investigated within-session dynamics of neocortical alpha (7-14 Hz) rhythms and their allocation with cued-attention using MEG recorded from primary somatosensory neocortex (SI) in humans. We found that there were significant and systematic changes across a single ~1 hour recording session in several dimensions, including increased alpha power, increased differentiation in attention-induced alpha allocation, increased distinction in immediate time-locked post-cue evoked responses in SI to different visual cues, and enhanced power in the immediate cue-locked alpha band frequency response. Further, comparison of two commonly used baseline methods showed that conclusions on the evolution of alpha dynamics across a session were dependent on the normalization method used.

CONCLUSIONS

These findings are important not only as they relate to studies of oscillations in SI, they also provide a robust example of the type of dynamic changes in brain measures within a single session that are overlooked in most human brain imaging/recording studies.

Cortical sources of resting state electroencephalographic rhythms in Parkinson's disease related dementia and Alzheimer's disease

OBJECTIVE

Here we test the hypothesis that cortical source mapping of resting state electroencephalographic (EEG) rhythms could characterize neurodegenerative disorders inducing cognitive impairment such as Parkinson's disease related dementia (PDD) and Alzheimer's disease (AD).

METHODS

To address this issue, eyes-closed resting state EEG rhythms were recorded in 13 PDD, 20 AD, and 20 normal elderly (Nold) subjects. Age, gender, and education were carefully matched across the three groups. Mini Mental State Evaluation (MMSE) score probed subjects' global cognitive status, and was matched between the PDD and AD groups. EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz). EEG cortical sources were estimated by low resolution brain electromagnetic source tomography (LORETA).

RESULTS

With respect to the Nold and AD groups, the PPD group was characterized by peculiar abnormalities of central delta sources and posterior cortical sources of theta and beta1 rhythms. With respect to the Nold group, the PDD and AD groups mainly pointed to lower posterior cortical sources of alpha1 rhythms, which were positively correlated to MMSE score across all PDD and AD subjects as a whole (the lower the alpha sources, the lower the MMSE score). This alpha decrease was greater in the AD than PPD patients.

CONCLUSIONS

The results suggest that topography and frequency of eyes-closed resting state cortical EEG rhythms distinguished PDD and AD groups.

SIGNIFICANCE

We report the existence of different effects of neurodegeneration on the cortical neural synchronization mechanisms generating resting state EEG rhythms in PDD and AD patients.

Functional network disruption in the degenerative dementias

Despite advances towards understanding the molecular pathophysiology of the neurodegenerative dementias, the mechanisms linking molecular changes to neuropathology and neuropathological changes to clinical symptoms remain largely obscure. Connectivity is a distinctive feature of the brain and the integrity of functional network dynamics is crucial for normal functioning. A better understanding of network disruption in the neurodegenerative dementias might help bridge the gap between molecular changes, pathological changes, and symptoms. Recent findings on functional network disruption as assessed with resting-state or intrinsic connectivity functional MRI and electroencephalography and magnetoencephalography have shown distinct patterns of network disruption across the major neurodegenerative diseases. These network abnormalities are somewhat specific to the clinical syndromes and, in Alzheimer's disease and frontotemporal dementia, network disruption tracks the pattern of pathological changes. These findings might have practical implications for diagnostic accuracy, allowing earlier detection of neurodegenerative diseases even at the presymptomatic stage, and tracking of disease progression.

Brain graphs: graphical models of the human brain connectome

Brain graphs provide a relatively simple and increasingly popular way of modeling the human brain connectome, using graph theory to abstractly define a nervous system as a set of nodes (denoting anatomical regions or recording electrodes) and interconnecting edges (denoting structural or functional connections). Topological and geometrical properties of these graphs can be measured and compared to random graphs and to graphs derived from other neuroscience data or other (nonneural) complex systems. Both structural and functional human brain graphs have consistently demonstrated key topological properties such as small-worldness, modularity, and heterogeneous degree distributions. Brain graphs are also physically embedded so as to nearly minimize wiring cost, a key geometric property. Here we offer a conceptual review and methodological guide to graphical analysis of human neuroimaging data, with an emphasis on some of the key assumptions, issues, and trade-offs facing the investigator.

Utility of imaging for nutritional intervention studies in Alzheimer's disease

Alzheimer's disease (AD) is a multi-factorial neurodegenerative disorder and the leading cause of dementia, wherein synapse loss is the strongest structural correlate with cognitive impairment. Basic research has shown that dietary supply of precursors and co-factors for synthesis of neuronal membranes enhances the formation of synapses. Daily intake of a medical food containing a mix of these nutrients for 12 weeks in humans improved memory, measured as immediate and delayed verbal recall by the Wechsler Memory Scale-revised, in patients with very mild AD (MMSE 24-26). An improvement of immediate verbal recall was noted following 24 weeks of intervention in an exploratory extension of the study. These data suggest that the intervention may improve synaptic formation and function in early AD. Here we review emerging technologies that help identify changes in pathological hallmarks in AD, including synaptic function and loss of connectivity in the early stages of AD, before cognitive and behavioural symptoms are observable. These techniques include the detection of specific biomarkers in the cerebrospinal fluid, as well as imaging procedures such as fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET), amyloid PET, structural/functional magnetic resonance imaging, diffusion tensor imaging, magnetoencephalography (MEG) and electroencephalography (EEG). Such techniques can provide new insights into the functional and structural changes in the brain over time, and may therefore help to develop more effective AD therapies. In particular, nutritional intervention studies that target synapse formation and function may benefit from these techniques, especially FDG-PET and EEG/MEG employed in the preclinical or early stages of the disease.

Magnetoencephalography as a putative biomarker for Alzheimer's disease

Alzheimer's Disease (AD) is the most common dementia in the elderly and is estimated to affect tens of millions of people worldwide. AD is believed to have a prodromal stage lasting ten or more years. While amyloid deposits, tau filaments, and loss of brain cells are characteristics of the disease, the loss of dendritic spines and of synapses predate such changes. Popular preclinical detection strategies mainly involve cerebrospinal fluid biomarkers, magnetic resonance imaging, metabolic PET scans, and amyloid imaging. One strategy missing from this list involves neurophysiological measures, which might be more sensitive to detect alterations in brain function. The Magnetoencephalography International Consortium of Alzheimer's Disease arose out of the need to advance the use of Magnetoencephalography (MEG), as a tool in AD and pre-AD research. This paper presents a framework for using MEG in dementia research, and for short-term research priorities.

Mapping Functional Connectivity between Neuronal Ensembles with Larval Zebrafish Transgenic for a Ratiometric Calcium Indicator

The ability to map functional connectivity is necessary for the study of the flow of activity in neuronal circuits. Optical imaging of calcium indicators, including FRET-based genetically encoded indicators and extrinsic dyes, is an important adjunct to electrophysiology and is widely used to visualize neuronal activity. However, techniques for mapping functional connectivities with calcium imaging data have been lacking. We present a procedure to compute reduced functional couplings between neuronal ensembles undergoing seizure activity from ratiometric calcium imaging data in three steps: (1) calculation of calcium concentrations and neuronal firing rates from ratiometric data; (2) identification of putative neuronal populations from spatio-temporal time-series of neural bursting activity; and then, (3) derivation of reduced connectivity matrices that represent neuronal population interactions. We apply our method to the larval zebrafish central nervous system undergoing chemoconvulsant-induced seizures. These seizures generate propagating, central nervous system-wide neural activity from which population connectivities may be calculated. This automatic functional connectivity mapping procedure provides a practical and user-independent means for summarizing the flow of activity between neuronal ensembles.

It's about Time

The purpose of this review/opinion paper is to argue that human cognitive neuroscience has focused too little attention on how the brain may use time and time-based coding schemes to represent, process, and transfer information within and across brain regions. Instead, the majority of cognitive neuroscience studies rest on the assumption of functional localization. Although the functional localization approach has brought us a long way toward a basic characterization of brain functional organization, there are methodological and theoretical limitations of this approach. Further advances in our understanding of neurocognitive function may come from examining how the brain performs computations and forms transient functional neural networks using the rich multi-dimensional information available in time. This approach rests on the assumption that information is coded precisely in time but distributed in space; therefore, measures of rapid neuroelectrophysiological dynamics may provide insights into brain function that cannot be revealed using localization-based approaches and assumptions. Space is not an irrelevant dimension for brain organization; rather, a more complete understanding of how brain dynamics lead to behavior dynamics must incorporate how the brain uses time-based coding and processing schemes.

Mapping the Alzheimer's brain with connectomics

Alzheimer's disease (AD) is the most common form of dementia. As an incurable, progressive, and neurodegenerative disease, it causes cognitive and memory deficits. However, the biological mechanisms underlying the disease are not thoroughly understood. In recent years, non-invasive neuroimaging and neurophysiological techniques [e.g., structural magnetic resonance imaging (MRI), diffusion MRI, functional MRI, and EEG/MEG] and graph theory based network analysis have provided a new perspective on structural and functional connectivity patterns of the human brain (i.e., the human connectome) in health and disease. Using these powerful approaches, several recent studies of patients with AD exhibited abnormal topological organization in both global and regional properties of neuronal networks, indicating that AD not only affects specific brain regions, but also alters the structural and functional associations between distinct brain regions. Specifically, disruptive organization in the whole-brain networks in AD is involved in the loss of small-world characters and the re-organization of hub distributions. These aberrant neuronal connectivity patterns were associated with cognitive deficits in patients with AD, even with genetic factors in healthy aging. These studies provide empirical evidence to support the existence of an aberrant connectome of AD. In this review we will summarize recent advances discovered in large-scale brain network studies of AD, mainly focusing on graph theoretical analysis of brain connectivity abnormalities. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis and monitoring.

Alpha and theta rhythm abnormality in Alzheimer's Disease: a study using a computational model

Electroencephalography (EEG) studies in Alzheimer's Disease (AD) patients show an attenuation of average power within the alpha band (7.5-13 Hz) and an increase of power in the theta band (4-7 Hz). Significant body of evidence suggest that thalamocortical circuitry underpin the generation and modulation of alpha and theta rhythms. The research presented in this chapter is aimed at gaining a better understanding of the neuronal mechanisms underlying EEG band power changes in AD which may in the future provide useful biomarkers towards early detection of the disease and for neuropharmaceutical investigations. The study is based on a classic computational model of the thalamocortical circuitry which exhibits oscillation within the theta and the alpha bands. We are interested in the change in model oscillatory behaviour corresponding with changes in the connectivity parameters in the thalamocortical as well as sensory input pathways. The synaptic organisation as well as the connectivity parameter values in the model are modified based on recent experimental data from the cat thalamus. We observe that the inhibitory population in the model plays a crucial role in mediating the oscillatory behaviour of the model output. Further, increase in connectivity parameters in the afferent and efferent pathways of the inhibitory population induces a slowing of the output power spectra. These observations may have implications for extending the model for further AD research.

Analysis of spontaneous MEG activity in mild cognitive impairment using spectral entropies and disequilibrium measures

The aim of this study was to explore the ability of several spectral entropies and disequilibrium measures to discriminate between spontaneous magnetoencephalographic (MEG) oscillations from 18 mild cognitive impairment (MCI) patients and 24 controls. The Shannon spectral entropy (SSE), Tsallis spectral entropy (TSE), and Rényi spectral entropy (RSE) were calculated from the normalized power spectral density to evaluate the irregularity patterns. In addition, the Euclidean (ED) and Wootters (WD) distances were computed as disequilibrium measures. Results revealed statistically significant lower SSE and TSE(2) values for MCI patients than for controls (p < 0.05) in the right lateral region of the brain. ED also obtained statistically significant lower values for MCI patients than for controls using the (p < 0.05) in the right lateral region of the brain. These findings suggest that MCI is associated with a significant decrease in irregularity of MEG activity. In addition, the highest accuracy of 64.3% was achieved by the SSE. We conclude that measures from information theory can be useful to both characterize abnormal brain dynamics and help in MCI detection.

Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury

Cognitive processes require a functional interaction between specialized multiple, local and remote brain regions. Although these interactions can be strongly altered by an acquired brain injury, brain plasticity allows network reorganization to be principally responsible for recovery. The present work evaluates the impact of brain injury on functional connectivity patterns. Networks were calculated from resting-state magnetoencephalographic recordings from 15 brain injured patients and 14 healthy controls by means of wavelet coherence in standard frequency bands. We compared the parameters defining the network, such as number and strength of interactions as well as their topology, in controls and patients for two conditions: following a traumatic brain injury and after a rehabilitation treatment. A loss of delta- and theta-based connectivity and conversely an increase in alpha- and beta-band-based connectivity were found. Furthermore, connectivity parameters approached controls in all frequency bands, especially in slow-wave bands. A correlation between network reorganization and cognitive recovery was found: the reduction of delta-band-based connections and the increment of those based on alpha band correlated with Verbal Fluency scores, as well as Perceptual Organization and Working Memory Indexes, respectively. Additionally, changes in connectivity values based on theta and beta bands correlated with the Patient Competency Rating Scale. The current study provides new evidence of the neurophysiological mechanisms underlying neuronal plasticity processes after brain injury, and suggests that these changes are related with observed changes at the behavioural level.

Development of a large-scale functional brain network during human non-rapid eye movement sleep

Graph theoretical analysis of functional magnetic resonance imaging (fMRI) time series has revealed a small-world organization of slow-frequency blood oxygen level-dependent (BOLD) signal fluctuations during wakeful resting. In this study, we used graph theoretical measures to explore how physiological changes during sleep are reflected in functional connectivity and small-world network properties of a large-scale, low-frequency functional brain network. Twenty-five young and healthy participants fell asleep during a 26.7 min fMRI scan with simultaneous polysomnography. A maximum overlap discrete wavelet transformation was applied to fMRI time series extracted from 90 cortical and subcortical regions in normalized space after residualization of the raw signal against unspecific sources of signal fluctuations; functional connectivity analysis focused on the slow-frequency BOLD signal fluctuations between 0.03 and 0.06 Hz. We observed that in the transition from wakefulness to light sleep, thalamocortical connectivity was sharply reduced, whereas corticocortical connectivity increased; corticocortical connectivity subsequently broke down in slow-wave sleep. Local clustering values were closest to random values in light sleep, whereas slow-wave sleep was characterized by the highest clustering ratio (gamma). Our findings support the hypothesis that changes in consciousness in the descent to sleep are subserved by reduced thalamocortical connectivity at sleep onset and a breakdown of general connectivity in slow-wave sleep, with both processes limiting the capacity of the brain to integrate information across functional modules.

Altered resting state cortico-striatal connectivity in mild to moderate stage Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder that is characterized by dopamine depletion in the striatum. One consistent pathophysiological hallmark of PD is an increase in spontaneous oscillatory activity in the basal ganglia thalamocortical networks. We evaluated these effects using resting state functional connectivity MRI in mild to moderate stage Parkinson's patients on and off l-DOPA and age-matched controls using six different striatal seed regions. We observed an overall increase in the strength of cortico-striatal functional connectivity in PD patients off l-DOPA compared to controls. This enhanced connectivity was down-regulated by l-DOPA as shown by an overall decrease in connectivity strength, particularly within motor cortical regions. We also performed a frequency content analysis of the BOLD signal time course extracted from the six striatal seed regions. PD off l-DOPA exhibited increased power in the frequency band 0.02–0.05 Hz compared to controls and to PD on l-DOPA. The l-DOPA associated decrease in the power of this frequency range modulated the l-DOPA associated decrease in connectivity strength between striatal seeds and the thalamus. In addition, the l-DOPA associated decrease in power in this frequency band correlated with the l-DOPA associated improvement in cognitive performance. Our results demonstrate that PD and l-DOPA modulate striatal resting state BOLD signal oscillations and cortico-striatal network coherence.

MEG connectivity analysis in patients with Alzheimer's disease using cross mutual information and spectral coherence

Alzheimer's disease (AD) is an irreversible brain disorder which represents the most common form of dementia in western countries. An early and accurate diagnosis of AD would enable to develop new strategies for managing the disease; however, nowadays there is no single test that can accurately predict the development of AD. In this sense, only a few studies have focused on the magnetoencephalographic (MEG) AD connectivity patterns. This study compares brain connectivity in terms of linear and nonlinear couplings by means of spectral coherence and cross mutual information function (CMIF), respectively. The variables defined from these functions provide statistically significant differences (p < 0.05) between AD patients and control subjects, especially the variables obtained from CMIF. The results suggest that AD is characterized by both decreases and increases of functional couplings in different frequency bands as well as by an increase in regularity, that is, more evident statistical deterministic relationships in AD patients' MEG connectivity. The significant differences obtained indicate that AD could disturb brain interactions causing abnormal brain connectivity and operation. Furthermore, the combination of coherence and CMIF features to perform a diagnostic test based on logistic regression improved the tests based on individual variables for its robustness.

Magnetoencephalography in neuropsychiatry: ready for application?

PURPOSE OF REVIEW

Magnetoencephalography (MEG) has been available for over 30 years, but the past 10 years have seen serious investigation of its use as a clinical tool. It is therefore an opportune time to review how MEG is able to contribute to neuropsychiatric research and practice.

RECENT FINDINGS

We limit this review to the areas of dementia, schizophrenia, depression and autism. MEG can achieve correct classification of individuals with mild cognitive impairment versus Alzheimer's disease, may identify a marker of early disease in schizophrenia, can distinguish bipolar from major depressive disorder, and has been used to study cognitive and other deficits in autism. It provides a valuable tool to study cognitive dysfunction.

SUMMARY

The most important aspect of MEG is the ability to record neural activity with millisecond precision, allowing coherence analysis of neural data to examine how brain areas are synchronized. Such synchrony is thought to underlie cognitive processes. As cognitive dysfunction is a common marker of neuropsychiatric disorders, MEG is emerging as an important investigatory tool in neuropsychiatry. It may also be useful clinically for early or differential diagnosis of some neuropsychiatric disorders, or for the prediction of drug effects, but more research is necessary before this becomes a clinical reality.