. Apolipoprotein E epsilon4 is associated with disease-specific effects on brain atrophy in Alzheimer's disease and frontotemporal dementia. Proc Natl Acad Sci U S A. 2009 Feb 10;106(6):2018-22. PubMed.


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  1. Since its introduction, the electroencephalogram (EEG) was viewed with great enthusiasm as the only methodology allowing a direct and online view of the “brain at work.” In the last decades, the introduction of structural and metabolic brain imaging methods (i.e., PET, MRI, and fMRI) have relegated neurophysiological techniques to such a relatively low level of interest for diagnosis and research in Alzheimer disease and related forms of dementias that clinical guidelines do not include EEG recordings as a primary step for correct diagnosis. However, despite the fact that modern methods of functional brain imaging combine extremely precise anatomical details to inform on brain metabolism and function (very high spatial resolution), the question has been more frequently raised as to how could the temporal resolution (that is, the ability to follow information processing in brain circuitries with a time sampling not of minutes or seconds, but at a millisecond level, which is the proper brain timing) be improved. Moreover, it is becoming evident that indirect information stemming from neurovascular coupling (that is, the linkage between the firing characteristics of neurons attending a given task and their blood supply and oxygen-glucose consumption) is only partly known, with a number of exceptions to the general rules, which makes large-scale clinical applications of these imaging modalities (MRI, PET, fMRI) still premature. Finally, PET and fMRI are expensive devices, available only in highly specialized centers and—at least in the case of PET—the use of ionizing radioisotopes is required; both reasons inhibit short-term follow-up exams in large groups of subjects.

    For all the above reasons, in recent years the “old” EEG has been revisited by using a number of modern approaches that can analyze and localize sources of EEG rhythms and signals in 3D, as well as track neural wiring and connectivity that characterize the hierarchy of the electromagnetic brain activity sustaining a given function. The approach is gradually regaining the interest of the scientific community as a tool theoretically able to discern—with a time resolution that follows the “brain time,” that is, in the order of milliseconds or even fractions of milliseconds—the sequential recruitment of relays within networks sustaining the investigated task, according to its “natural” hierarchy. This ability is not really surprising, if one considers that the same neuronal circuits responsible for behavior, mood, emotions, memory, movements, sensations, language, and attention produce electromagnetic transient or rhythmic signals that vary in time in parallel with the evolving cognitive process. It is, therefore, one of the most fascinating challenges of modern neuroscience to disentangle cerebral electromagnetic activities causally linked to a specific brain function from the bulk of spontaneous transient and oscillating brain waves.

    This study by Teresa Montez and her colleagues in Amsterdam approaches the analysis of electromagnetic brain signals in a relatively new way, namely by evaluating time-varying characteristics of the main rhythms of oscillation, looking at their interactions across different brain areas within a given instant (cross-correlation analysis) as well as within the same brain area in a relatively extended time window and examines auto-correlation properties (that is the coordination, if any, of rhythmic brain activity in time within a given cortical district), which is linked to serial processing. In a group of 19 patients affected by early-stage Alzheimer disease, the authors found a significant reduction of time fluctuations of α rhythms (as depicted by sequential representation of high- and low-amplitude bursts of such rhythm) over temporo-parietal brain regions, combined with weaker than normal autocorrelations on long-time scales (time window between 1 and 25 seconds). Meanwhile, the same parameters for ϑ rhythms were significantly increased on medial, prefrontal brain regions. They interpret α changes as secondary to damage to cholinergic, encoding/retrieval memory mechanisms, while ϑ modifications in the prefrontal cortex are considered compensatory mechanisms pointing toward functional maintenance. Follow-up studies will confirm or disconfirm this interesting hypothesis.

    Within this vein of reasoning we should finally consider that spatio-temporal characteristics of electromagnetic brain signals contain relevant information on pathologic processes underlying different types of dementias. It is worth remembering that the integrated analysis of EEG power and coherence provided reliable predictions of MCI to AD progression within a relatively short time-frame (about one and a half years), demonstrating that low temporal δ source and low γ band coherence along the fronto-parietal midline correctly predict around 10 percent of annual rate of conversion to AD. The progression to AD conversion was predicted to be faster across about one year in individual MCI subjects with δ sources and fronto-parietal coherence higher than cut-off points obtained from control population. Moreover, methods producing “clusters” (Supervised Artificial Neuronal Networks; see Rossini et al., 2008) are able to classify subjects on an individual basis, i.e., whether they fall within or outside a normative cluster, as is required for clinical purposes, with a sensitivity and a specificity around 90 percent.

    Actually, a bulk of recent evidence points toward the idea that loss of synaptic contacts and neuronal connectivity begins longer before onset of clinical deficits in AD patients, and that they are compensated by neuroplastic phenomena, mainly based on recruitment of vicarious networks and enhanced network excitability. In a recent editorial in Annals of Neurology, commenting upon promising neuropsychological techniques for detection of prodromal (namely preclinical) signs of AD, Mortimer and Petersen (2009) speculated that if disease-modifying therapies will be found, they should be most effective when administered well in advance of symptom onset in at-risk individuals. This would urge us to find reliable biomarkers to detect such high-risk subjects many years before AD onset. Since the number of potentially at-risk individuals is extremely elevated, and widely distributed across the globe, such biomarkers should be low-cost, non-invasive, and widely available (otherwise, no health system could afford large-scale screening of general population); moreover, because of the ethical and sociological implications of such preclinical diagnosis, biomarkers should be extremely sensitive and specific, with an acceptable level of false positive and negative identifications. Needless to say, no biomarkers with such characteristics are currently available. However, neurophysiological techniques (namely EEG) do have many of the required properties since they are harmless, low-cost, and widely available on a worldwide basis. By using modern methods of signal analysis, EEG analysis is also becoming more and more sensitive and specific (see Rossini et al., 2007 for a review). The study by Montez et al. represents a further step toward this goal and even if they are using MEG (an expensive and relatively little diffused device), their type of analysis can be easily translated to EEG recordings. It is concluded that experts and professionals in the field of health organizations as well as drug companies and patients’ associations should look with careful and growing interest (and eventually invest some more resources) to the recent developments reached by neurophysiological methods within this field.


    . Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease. Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1614-9. PubMed.

    . Detection of prodromal Alzheimer's disease. Ann Neurol. 2008 Nov;64(5):479-80. PubMed.

    . Clinical neurophysiology of aging brain: from normal aging to neurodegeneration. Prog Neurobiol. 2007 Dec;83(6):375-400. PubMed.

    . Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?. Clin Neurophysiol. 2008 Jul;119(7):1534-45. PubMed.

    View all comments by Paolo Maria Rossini

This paper appears in the following:


  1. Measuring Gross Brain Changes in AD, FTD
  2. ApoE and Brain Networks—The Anatomy of a Risk Factor