Abnormal cortical sources of resting state electroencephalographic ...

Clinical Neurophysiology 127 (2016) 1803–1812

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Abnormal cortical sources of resting state electroencephalographic rhythms in single treatment-naïve HIV individuals: A statistical z-score index Claudio Babiloni a,b,⇑, Alfredo Pennica c, Claudio Del Percio d, Giuseppe Noce d, Susanna Cordone a, Chiara Muratori b, Stefano Ferracuti e, Nicole Donato e, Francesco Di Campli c, Laura Gianserra c, Elisabetta Teti c, Antonio Aceti c, Andrea Soricelli d,f, Magdalena Viscione g, Cristina Limatola a, Massimo Andreoni g, Paolo Onorati a,b a

Department of Physiology and Pharmacology, University of Rome ‘‘La Sapienza”, Rome, Italy IRCCS S. Raffaele Pisana, Rome, Italy c Infectious Diseases, Faculty of Medicine and Psychology, University of Rome ‘‘La Sapienza”, Rome, Italy d IRCCS SDN, Naples, Italy e Psychiatry, Faculty of Medicine and Psychology, University of Rome "La Sapienza", Rome, Italy f Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy g Clinical Infectious Diseases, University of Rome ‘‘Tor Vergata”, Rome, Italy b

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Article history: Accepted 5 December 2015 Available online 19 December 2015 Keywords: Human immunodeficiency virus (HIV) Resting-state electroencephalography (EEG) Low-resolution brain electromagnetic source tomography (LORETA) Z-score Delta/alpha power density

h i g h l i g h t s
This pilot study tested a statistical z-score procedure to identify single treatment-naïve HIV individ-

uals having abnormal resting state electroencephalographic (EEG) sources.
Compared to HIV individuals with normal EEG sources, those (47.6%) with abnormal z-score values

showed worse cognitive and serological markers.
This procedure is promising to assess effects of HIV on brain function in single treatment-naïve HIV

individuals.

a b s t r a c t Objective: This study tested a simple statistical procedure to recognize single treatment-naïve HIV individuals having abnormal cortical sources of resting state delta ( control healthy group was fitted by parietal delta sources (p < 0.005); (ii) the EEG source pattern pointing to treatment-naïve HIV group < control healthy group was fitted by parietal, occipital, temporal, and limbic alpha-2 and alpha-3 sources (p < 0.0005). The present results confirm that parietal cortical sources of resting state eyes-closed delta and highfrequency alpha rhythms are good candidates for the computation of a valid EEG marker for a neurophysiologic assessment of treatment-naïve HIV patients. Based on these results, we defined as EEG variable of interest (marker) the ratio of the activity between parietal delta and alpha-3 sources. This EEG marker was used as an input for the classification between healthy and treatment-naïve HIV individuals and the computation of z-score in all treatment-naïve HIV subjects.

Fig. 1. Grand average of low resolution brain electromagnetic tomography (LORETA) solutions (i.e. normalized current density at the cortical voxels) modeling the distributed electroencephalographic (EEG) cortical sources for delta, theta, alpha 1, alpha 2, alpha 3, beta 1, and beta 2 bands in healthy and treatment-naïve HIV groups. These frequency bands are defined with respect to the individual alpha peak (IAF) as follows: (i) delta, IAF 8 Hz to IAF 6 Hz; (ii) theta, IAF 6 Hz to IAF 4 Hz; (iii) alpha-1, IAF 4 Hz to IAF 2 Hz; (iv) alpha-2, IAF 2 Hz to IAF; and (v) alpha-3, IAF to IAF +2 Hz. Furthermore, for the higher frequencies, we selected standard fixed frequency bands as follow: (i) beta 1 (13–20 Hz); and (ii) beta 2 (20–30 Hz). Color scale: a normalization of the data was obtained by normalizing the LORETA current density at each voxel with the current density averaged across all frequencies (0.5–45 Hz) and across all 2394 voxels of the brain volume. The color scale of the figure ranges from 0 to the maximum value of the normalized current density estimated for alpha 2 frequency band. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 2. Mean regional normalized LORETA solutions relative to a statistical ANOVA interaction F(30,4170 = 25.9; p < 0.0001) among the factors group (treatment-naïve HIV and healthy; independent variable), Band (delta, theta, alpha 1, alpha 2, alpha 3, beta 1, beta 2), and ROI (central, frontal, parietal, occipital, temporal, limbic).

Fig. 4. Z-scores of the EEG marker (ratio between the activity of parietal delta and alpha-3 sources) in all treatment-naïve HIV subjects. In the figure, the dashed line indicates the threshold of statistical difference (p < 0.05, one tailed) that splits all treatment-naïve HIV individuals in two sub-groups: the sub-group of treatmentnaïve HIV subjects with a statistically abnormal EEG marker (i.e. EEG+, above the line) and the sub-group of those with a normal EEG marker (i.e. EEG, below the line).

of area under the ROC curve (see Fig. 3). These results indicated that the EEG marker allows a moderate classification of the individuals of the two populations (i.e. healthy, treatment-naïve HIV). Fig. 3. ROC (receiver operating characteristic) curve illustrating the performance of the EEG marker (i.e. ratio between parietal delta and alpha-3 sources) in the classification of the healthy and treatment-naïve HIV individuals.

3.2. Accuracy of the EEG marker in the classification between healthy and treatment-naïve HIV individuals The EEG marker served as a discriminant variable for the ROC analysis in the classification between the healthy and treatmentnaïve HIV individuals. Results showed 70% of sensitivity in the correct recognition of the treatment-naïve HIV patients (true positive rate, expressed as a percentage), 81% of specificity in the correct recognition of the healthy subjects (true negative rate), and 0.80

3.3. Z-score of EEG cortical sources as estimated by LORETA Fig. 4 plots the z-scores of the EEG marker (ratio between the activity of parietal delta and alpha-3 sources) in all treatmentnaïve HIV subjects. In the figure, the dashed line indicates the threshold of statistical difference (p < 0.05, one tailed) that splits all treatment-naïve HIV individuals in two sub-groups: the subgroup of treatment-naïve HIV subjects with a statistically abnormal EEG marker (i.e. EEG+, above the line) and the sub-group of those with a normal EEG marker (i.e. EEG, below the line). Interestingly, there was a relatively high percentage (i.e. 47.6%) of treatment-naïve HIV subjects with values of z-score indicating a statistically abnormal EEG marker (i.e. EEG+). Furthermore, Table 3

C. Babiloni et al. / Clinical Neurophysiology 127 (2016) 1803–1812

reports the values of t tests computed to evaluate the differences between the two sub-groups of treatment-naïve HIV subjects (i.e. EEG+ and EEG) for each dependent variable of interest (i.e. age, education, MMSE score, IAF, CD4 count, and VL). Results showed that compared to the treatment-naïve HIV subjects with EEG, the treatment-naïve HIV subjects with EEG+ presented lower global cognitive status, as revealed by MMSE score, and abnormal serological indexes such as lower CD4 count and higher VL (p < 0.05). To better evaluate the relationship between the present EEG marker and MMSE score, a Spearman correlation analysis was performed across all healthy and treatment-naïve HIV subjects (p < 0.05). Results confirmed the statistically significant correlation between these two variables (p < 0.005). The higher the value of the EEG marker (as a sign of statically abnormal EEG marker), the lower the MMSE score (as a sign of cognitive status).

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free 2 s EEG segments. Pearson test was used for these analyses (p < 0.05). Results showed no statistically significant correlation in both analyses (p > 0.05). To cross-validate LORETA cortical source estimates, we performed a control analysis by computing topographic maps of EEG power density for the most representative bands of interest (i.e. delta, alpha 2 and alpha 3) and for the healthy and the treatment-naïve HIV group (see Fig. 5). In line with normalization procedure used for LORETA source estimation, EEG power density

3.4. Control analyses Noteworthy, it may be argued that the above statistical effect depended on the difference of age and education between the two sub-groups of treatment-naïve HIV subjects (i.e. EEG+ and EEG). To test it, a control analysis was performed on subpopulations of healthy and treatment-naïve HIV subjects matched for these demographic variables (see the subjects’ features in Table 4). Results confirmed that compared to the sub-group of treatment-naïve HIV subjects with EEG, the sub-group of those with EEG+ presented lower MMSE score, lower CD4 count, and higher VL (p < 0.05; see Table 4). It may also be argued that the above statistical effect depended on the amount of EEG segments used for the EEG source estimation in the treatment-naïve HIV subjects. This control hypothesis was tested by two analyses. A first control analysis tested if z-score of the EEG marker was correlated across all treatment-naïve HIV subjects with the duration of the original resting state EEG recording. Another control analysis tested if z-score of the EEG marker was correlated across all these subjects with the amount of artifact-

Fig. 5. Spatial distribution of EEG power density for most representative EEG bands (i.e. delta, alpha 2 and alpha 3) and for the two group of subjects (healthy and treatment-naïve HIV).

Table 3 Means (±SE) of the personal and clinical features of the sub-group of treatment-naïve HIV subjects with a statistically abnormal EEG marker (i.e. EEG+) and the sub-group of those with a normal EEG marker (i.e. EEG). The third column report the results of t-test computed between the two groups (i.e. EEG vs. EEG+). Some statistical differences are reported (p < 0.05), including age and education.

N Age (years) Education (years) MMSE score IAF (Hz) CD4 count (cells/ ll) VL (copies/ml)

EEG

EEG+

P values

43 37.5 (±1.7 SE) 14.4 (±0.4 SE) 29.0 (±0.3 SE) 10.3 (±0.1 SE) 501.0 (±39.7 SE)

39 42.4 (±1.7 SE) 13.1 (±0.5 SE) 27.3 (±0.5 SE) 10.1 (±0.1 SE) 372.4 (±40.5 SE)

p = 0.04 p = 0.03 p = 0.001 n.s. p = 0.01

81155.8 (±17218.4 SE)

230534.1 (±83533.1 SE)

p = 0.03

Fig. 6. Topographical maps of statistical values at electrode (sensor) level for frequency bands that presented most statistical significant differences between healthy and treatment-naïve HIV groups (i.e. delta, alpha 2 and alpha 3).

Table 4 Means (±SE) of the personal and clinical features of two sub-groups of EEG and EEG+ treatment-naïve HIV subjects. These two sub-groups were formed to be matched for age and education from the whole HIV cohort of Table 4. The third column report the results of t-test computed between the two sub-groups (i.e. EEG vs. EEG+).

N Age (years) Education (years) MMSE score IAF (Hz) CD4 count (cells/ll) VL (copies/ml)

EEG

EEG+

P values

40 38.6 (±1.7 SE) 14.4 (±0.5 SE) 29.1 (±0.3 SE) 10.3 (±0.1 SE) 498.6 (±42.6 SE) 83132.8 (±18431.0 SE)

36 40.9 (±1.5 SE) 13.5 (±0.5 SE) 27.4 (±0.5 SE) 10.1 (±0.1 SE) 366.3 (±42.4 SE) 240777.5 (±88044.6 SE)

n.s. n.s. p = 0.001 n.s. p = 0.02 p = 0.03

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at each scalp electrode was normalized to EEG power density averaged across all frequencies (0.5–45 Hz) and across all electrodes (N = 19). Fig. 6 plots the statistical t maps of scalp EEG power density for delta, alpha 2, and alpha 3. These maps showed the statistical differences of the EEG power density between the healthy and the treatment-naïve HIV group. They globally confirmed the differences between the two groups based on the comparison of the corresponding LORETA cortical source estimates. However, the spatial resolution of EEG power density mapped at scalp level appeared to be lower than that of the LORETA cortical source estimates. 4. Discussion In this pilot study, we tested for the first time a new simple procedure to identify single treatment-naïve HIV male individuals having statistically abnormal activity of these EEG cortical sources and cognitive deficits with reference to a control group of sex-, age-, and education-matched healthy individuals. Results of the present study confirmed that compared to a group of healthy male subjects, a large group of treatment-naïve HIV male patients presented higher activity of parietal delta sources and lower activity of widespread high-frequency alpha sources. Furthermore, there was a correlation between the EEG marker and MMSE score across the treatment-naïve HIV and healthy individuals. These results provided further evidence that the EEG marker probes a brain function related to global cognition in single human subjects. Furthermore, they complement and extend previous evidence obtained by the same methodology of EEG source estimation in smaller groups of treatment-naïve HIV subjects (Babiloni et al., 2012, 2014, 2015). Specifically, it was previously shown that treatment-naïve HIV subjects at group level were characterized by abnormalities of these sources associated with cognitive deficits, relevant serological parameters of the infection, and chronic treatment by cART (Babiloni et al., 2012, 2014, 2015). Furthermore, the results of a present control analysis confirm previous evidence of other research groups showing that amplitude of scalp posterior alpha (8–12 Hz) rhythms and cognitive abilities were lower in HIV patients with respect to control subjects, with a partial normalization of the EEG markers by cART (Gruzelier et al., 1996; Harrison et al., 1998; Baldeweg et al., 1997). Overall, the present results suggest that topographically widespread cortical sources of resting state delta and alpha rhythms reflect neurophysiologic abnormalities in treatment-naïve HIV subjects, at least at a group level. As original results of the present study, z-score of an EEG marker of interest was calculated in any treatment-naïve HIV patient with reference to the group of age- and education-matched healthy subjects. Remarkably, this control group should not be considered as representative of the general population, as it was tailored on the present cohort of HIV individuals. Therefore, the current results on the classification rate of the EEG markers between the present HIV and control individuals could not be representative for all sub-classes of treatment-naïve HIV individuals formed by different ranges of age, education (i.e. cognitive reserve), and either sexes or genders. Concerning the features of the general population of HIV individuals, it should be stressed that HIV infection is caused at any age by the exposition to blood, vaginal fluid, pre-ejaculate, semen, and breast milk. For this reason, HIV is not a rare disease that affects few people with genetic peculiarities or exclusive age range. According to Global Health Observatory (GHO) data (http://www.who.int), about 35 million people lived with HIV in 2013. About 0.8% of adults with an age of 15–49 years. Worldwide live with HIV, although HIV epidemics vary considerably between continents, countries, and regions. The present EEG marker was defined as the ratio between the activity of parietal delta and alpha-3 sources (i.e. the highest

frequency sub-band of alpha rhythms). The present results showed that this EEG marker was correlated with MMSE score (as an index of global cognitive status) in all healthy and treatment-naïve HIV subjects as a whole group (p < 0.005). Furthermore, the EEG marker was used to distinguish individual healthy and treatment-naive HIV subjects with a moderate classification rate of about 80%, as indexed by the area under the ROC curve. Compared to the subgroup of treatment-naïve HIV subjects with a statistically normal EEG marker (i.e. EEG; p > 0.05), the sub-group of those with a statistically abnormal EEG marker (i.e. EEG+; about 48% of all treatment-naïve HIV subjects) was characterized by lower global cognitive status, as revealed by MMSE score, and worse serological indexes such as lower CD4 count and higher VL. This was true even when age and education were matched between the two subgroups of treatment-naïve HIV subjects. A tentative explanation of the present novel results (z-scores unveiling about 40–50% of treatment-naïve HIV individuals with the abnormal EEG marker can be given taking into account general physiological meaning of resting state delta and alpha rhythms in humans. In the physiological condition of resting state eyes-closed, amplitude of cortical delta and theta rhythms is typically negligible, while cortical alpha rhythms are prominent in posterior visual, somato-motor and auditory cortical areas (Babiloni et al., 2006a; Klimesch, 1996; Klimesch et al., 1997, 1998; Pfurtscheller and Lopez da Silva, 1999). Maximum amplitude of alpha rhythms is typically observed in occipital and parietal areas for at least two reasons. Firstly, occipital and parietal cortical areas have large extension and adequate mantle envelope for a massive summation of synaptic currents at scalp surface level (Rossini et al., 1991; Klimesch, 1996; Klimesch et al., 1997, 1998; Pfurtscheller and Lopez da Silva, 1999). Secondly, synchronization of occipital and parietal alpha rhythms may sub-serve intensive cycles of excitation and inhibition that might gate (inhibit) the flow of visual, spatial, and somato-motor information from thalamus to cerebral cortex during resting state eyes-closed condition (Pfurtscheller and Lopez da Silva, 1999), while alpha desynchronization might frame perceptual events in discrete snapshots of around 70– 100 ms during active sensory and somato-motor information processing (Fingelkurts and Fingelkurts, 2006; Mathewson et al., 2009). In this theoretical framework, low-frequency (8–10.5 Hz) alpha rhythms would reflect the fluctuation of global cortical arousal and tonic attention during resting state condition (Klimesch, 1996; Klimesch et al., 1997, 1998; Rossini et al., 1991; Steriade and Llinás, 1988). Furthermore, high frequency (10.5–13 Hz) alpha rhythms would reflect the fluctuation of synchronization of cortical neural populations sub-serving semantic memory and somato-motor control (Klimesch, 1996; Klimesch et al., 1997, 1998; Rossini et al., 1991; Steriade and Llinás, 1988). The higher the amplitude of occipital and parietal alpha rhythms, the lower the local excitation of cortical neural populations as demonstrated by the co-registration of resting state EEG rhythms and functional MRI (Laufs et al., 2003; Gonçalves et al., 2006; Mantini et al., 2007). Such co-registration also emphasized the important role of thalamus in the generation of cortical alpha rhythms, namely the higher the cortical alpha power (as a sign of cortical inhibition), the higher the thalamic activation (Gonçalves et al., 2006). Keeping in mind the present findings and above theoretical overview, it can be speculated that in about 40–50% of treatment-naïve HIV individuals, infection might impair the functional connectivity between thalamus and topographically widespread cortical areas (especially parietal cortex) that physiologically inhibits corticofugal slow oscillations (