Longitudinal study of functional connectivity changes in asymptomatic neurocognitive impairment(ANI) in HIV- infected patients

doi:10.21203/rs.3.rs-3326926/v1

Background: Asymptomatic neurocognitive impairment (ANI) stage is a critical stage for early diagnosis and treatment of HIV-associated neurocognitive disorder(HAND) disease, but there are few longitudinal studies on it. The changes of cognitive function and pathogenesis in HIV infected patients during this period remain unclear. Our objective was to evaluate changes in brain function after treatment in ANI patients based on amplitude of low frequency fluctuation(ALFF), amplitude of Low frequency fluctuation (fALFF), regional homogeneity(ReHo), and functional connectivity(FC) obtained by resting state functional magnetic resonance imaging(rs-fMRI).

Methods: The study involved follow-up of 49 ANI HIV infected patients who underwent two rs-fMRI and neurocognitive tests with an average interval of 1.26 years. The ALFF/fALFF, Reho and FC changes between baseline group and follow-up group were discussed. Subsequently, correlations between rs-fMRI results and cognitive and clinical variables were analyzed.

Results: Compared with baseline group, the follow-up group had a significant ReHo decrease in the left median cingulate and paracingulate gyri (DCG.L), right calcarine fissure and surrounding cortex (CAL.R), MOG.R and left precental gyrus(PreCG.L), as well as ReHo enhancement in the left supramarginal gyrus (SMG.L), right postcentral gyrus (PoCG.R), right parahippocampal gyrus(PHG.R) and left calcarine fissure and surrounding cortex (CAL.L)(FWE corrected, voxel-level P < 0.001, cluster-level P < 0.05). The results of ALFF/fALFF were not corrected by multiple comparisons, but decreased in MOG, supplementary motor area (SMA) and posterior central gyrus, and increased in amygdala and insula. The matrix diagram shows a statistically significant difference in connection reduction between CAL.L and PreCG.L and between CAL.R and MOG.RSome of the results of ReHo and FC were correlated with the results of cognitive tests.

Conclusion: These findings suggest that abnormalities in the cingulate gyrus, peritalate cortex, and anterior central gyrus may indicate disease progression and cognitive decline, while abnormalities in the occipital lobe, visual center, and related functional connections should be of more concern. However, the increase of ReHo and FC in some brain regions may mean that the cognitive changes in ANI patients under treatment may be complex

ANI

HAND

ALFF

ReHo

FC

In the early stage of HIV infection (within 1 week), virus-infected macrophages enter the nerve center through the blood-brain barrier, causing chronic long-term damage to nerve cells^[1]. Even after antiretroviral therapy (cART), sensory and motor dysfunction and neurocognitive impairment, namely HIV-associated neurocognitive disorders (HAND), still occur in at least 50% of HIV infected people^[2].The course of HAND disease will go through three stages, Their symptoms ranged from asymptomatic neurocognitive impairment (ANI) to mild neurocognitive disorder (MND) in the preclinical phase. And eventually to HIV-associated dementia (HAD)^[3].ANI stage refers to the earliest stage with no clinical symptoms or very mild HAND neuropathological changes^[4]. At this stage, accurate diagnosis and targeted prevention and intervention may delay or reverse the pathophysiological process of HAND and prevent the occurrence of HAD. It has important scientific significance and clinical value for improving the prognosis and quality of life of patients with HAND^[5].

Resting state functional magnetic resonance imaging (rs-fMRI), a non-invasive, non-radioactive technique for observing brain activity, has been widely used in neuroscience and cognitive science. The main principle is blood oxygenation level dependent (BOLD)^[6]. In the human brain, oxygen is delivered to neurons by hemoglobin in capillaries. When neural activity increases, oxygen consumption increases, causing an increase in blood flow to the local area. When hemoglobin binds to oxygen molecules to form oxygenated hemoglobin, it is diamagnetic; And when the oxygen comes apart, it's paramagnetic^[7]. This magnetic difference causes a small change in the magnetic resonance signal, the basic principle of BOLD .Different methods can identify different spatial patterns in the brain by analyzing the specific frequency range shown in rs-fMRI^[8]. For the activity pattern between the remote regions in the whole brain, some so-called networks can be separated, such as default network, executive control and so on. Common methods include region of interest (ROI), independent component analysis (ICA), hierarchical clustering (HC). In addition, there are some local modes, including regional homogeneity (ReHo), amplitude of Low frequency fluctuation (ALFF), fractional amplitude of Low frequency fluctuation (fALFF), etc^[9–12].

Kendall s consistency coefficient (KCC) was used in ReHo's description, which can measure the consistency trend of multiple sequence steps and is a sort of consistency on ranking^[13]. ALFF/fALFF revealed BOLD signal intensity of spontaneous activity in the region. Spontaneous low frequency (0.01–0.08 Hz) fluctuations (LFF) in fMRI are highly synchronized between the left and right primary motor cortex at resting time. Therefore, it is generally believed that low frequency (0.01-0.08Hz) is basically nerve signal, lower than 0.01Hz is low-frequency drift, and higher than 0.08Hz is physiological noise^[14–16]. Ma’s research have found increased ALFF values in the right fusiform gyrus and the left parahippocampal gyrus in HIV + patients. Homogeneity of the right anterior cingulate gyrus and accessory cingulate gyrus and the left superior cerebellar lobe were decreased in HIV + patients^[17]. Sarma's research of perinatal HIV infected children (PHIV) found higher ALFF and ReHo in the medial orbital white matter of the brain compared to controls, and bilateral white matter showed increased spontaneous regional activity in PHIV patients. PHIV showed low frequency fluctuations that may affect glial circulation^[18]. Functional connectivity (FC) attempts to establish a connection between two spatial regions of interest with the help of linear temporal dependencies. The analysis based on seed points is the first resting state network identification method^[19]. Seed point based analysis is essentially a model-based approach where we can select a seed point or ROI and find a linear correlation between that seed point region and all the other voxels in the entire brain to get a seed based FC plot. Honnorat's research posits that alcoholic HIV participants had lower functional connectivity between the anterior cingulate cortex and the hippocampus, and that impaired connections between these pairs were significantly associated with greater lifetime total alcohol consumption^[20].

Based on these studies, our team also conducted a cross-sectional study of functional connectivity in ANI patients, and the results suggest that visual networks may be the most vulnerable brain regions in young ANI patients. The middle frontal gyrus, vermis cerebellum, and insula also play important roles in asymptomatic neurocognitive disorders^[21]. During the follow-up process, what changes will occur in the cognitive function of patients and the functional connection of which part of the brain area, and whether there is correlation with other serum biological indicators will be the main content of our study.

Participants

This study was approved by the medical ethics committee at Beijing You’an Hospital, and all the participants gave written informed consent in accordance with the Declaration of Helsinki. From November 2020 to December 2022, 49 HIV-infected patients with ANI were enrolled in the study and all completed 1 year follow-up with a mean interval of 1.26 years. We set our inclusion criteria because given that more than 95% of our patients are right-handed men and that there is clear evidence that gender and handedness have an impact on brain structure and function^[22–24], we excluded these factors that could affect our results.The inclusion criteria for all participants in our study were as follows: (1) Chinese male, (2) aged 20–50, and (3) righthand dominant. The exclusion criteria for all participants included the following: (1) presence of brain tumors, infection,stroke, epilepsy, and other diseases of the nervous system that can cause cognitive impairment according to medical history or conventional imaging exams; (2) depression, anxiety, or any other psychiatric illness; (3) history of substance abuse, alcoholism, and drugs; and (4) MR contraindication. (5)No major diseases that could affect brain structure or function during follow-up. For the HIV patients, the diagnosis was based on immunoassay western blot or through PCR, and the results are provided by professional medical institutions with diagnostic qualifications. Furthermore, only those infected by homosexual sex were recruited. The diagnostic classification for ANI according to the Frascati criteria meets the following four conditions^[25]: (1) performance in ≥ 2 cognitive domains at least one standard deviation below the mean of the demographicadjusted normative scores; (2) no decline in daily life function; (3) impairment does not meet the criteria for delirium or dementia; (4) no evidence that ANI is caused by other reasons. All participants underwent clinical examinations and neurocognitive tests before the MR scanning. Clinical data are presented in Table 1.

Neurocognitive tests

This study conducted detailed neurocognitive tests in six cognitive domains using proven psychological test versions^[26]. These tests reflected differences in the corresponding cognitive field and were analyzed as follows: (1) verbal and language (animal naming test); (2) attention and working memory [Wechsler memory scale-III (WMS-III), and Paced Auditory Serial Addition Test (PASAT)]; (3) memory, including learning and recall [Hopkins verbal learning test-revised (HVLT-R) and brief visuospatial memory test-revised (BVMT-R)]; (4) speed of information processing [trail-making test Part A (TMT A)]; (5) fine motor skills (Grooved Pegboard Test); and 6) abstraction and executive function [Wisconsin Card Sorting Test 64-card version (WCST-64)]^[2]. The raw scores for all tests were converted to T scores and adjusted for age, gender, years of education, and the population size of the cities where the subjects grew up.If one cognitive domain contained multiple cognitive tests, the average T-score for that cognitive domain was calculated. Participants underwent two neurocognitive tests. We adjusted the content and order of some questions in the two tests to reduce the interference caused by participants' impressions^[3].

MRI data acquisition

All imaging data were acquired on a 3.0T MR scanner (Tim-Trio, Siemens, Erlangen, Germany) using a 32-channel head coil. Tight foam pads were fixed to minimize movement during the scan. All subjects were instructed to lie quietly and relax, stay still, close their eyes but not fall asleep, and not think about anything in particular during the MRI scan. The rs-fMRI images were collected by using a gradient echo planar imaging sequence^[21]. The acquisition parameters were as follows: repetition time (TR), 2,000 ms; echo time (TE), 30 ms; flip angle, 90◦; resolution matrix, 64 × 64; field of view(FOV), 224 × 224 mm; section thickness, 3.5 mm; the number of sections, 35; and voxel size = 3.5 × 3.5 × 3.5 mm³. In total, 35 axial slices and 240-time points were obtained for each subject. In addition, a sagittal magnetization prepared gradient-echo (MPRAGE) sequence [repetition time (TR)/echo time (TE)/inversion time (TI) = 1,900/2.52/900 ms; acquisition matrix = 256 × 246; field of view = 250 × 250; flip angle = 9 degrees; voxel size = 1 mm × 0.977 mm × 0.977 mm] and axial T2-weighted fluid-attenuated inversion recovery (FLAIR) images (TR/TE/TI = 8000/2370.9/97; and matrix, 320 × 224) were also acquired for the exclusion of overt brain diseases such as stroke and demyelinating disease^[21].

Image pre-processing

Resting-state fMRI’s were pre-processed using the data processing and analysis for brain imaging (DPABI V4.5) toolbox. The first 10 time points were removed to ensure the adaptation of the participants to the scanning environment and the equilibrium of the magnetic field. The remaining 230-time points were used for further slice-timing correction and realignment correction for head motion. Any subject with a translation of more than 2.0 mm or a rotation of more than 2.0◦ during the MRI scan was excluded from the study. Reorient Fun and Reorient T1 must be checked in order to manually adjust functional and structural images, including head rotation, translation, origin positioning, and manually straighten the entire image. This has resulted in increased accuracy of coregistration, segmentation, and normalization^[28]. Next, the resulting rs-fMRI data were spatially normalized to the Montreal Neurological Institute (MNI) space and resampled to a voxel size of 3×3×3 mm³. After that, to remove the effects of low-frequency drift and high-frequency physiological noises, all data were subjected to linear-trend discarding and temporal bandpass filtering (0.01 Hz < f < 0.08 Hz). In addition, the head motion parameters, white matter, and cerebrospinal fluid signals were regressed to reduce the possible adverse effects^[29]. The Friston 24-parameter model was performed to remove the head motion effects. The mean framewise displacement (FD) was added to the statistical model as a covariate in group analyses to reduce the residual effects of motion. On the issue of regression to the global signal, we did some research and found that some studies showed that removing it could lead to an enhanced negative correlation or affect differences in functional connectivity^[30–31]. We obtained two results on whether to regression to the global signal or not, and finally adopted the result without regression. We chose Normalize by DARTEL to match the structural image to the functional image space, and then segmented the structural image into gray matter and white matter cerebrospinal fluid. At the same time, some transformation matrices were generated, so that a group template could be generated, and finally standardized to the MNI space.

Computation of ALFF/fALFF values and ReHo maps

The ALFF value of each voxel was calculated by averaging the square root of the power spectrum with 0.01–0.08 Hz.In order to reduce the effect of noise on ALFF, the ratio of ALFF to the oscillation amplitude of the full frequency range (0-0.25Hz) is used as an indicator to measure the amplitude of low-frequency oscillation (fALFF). Because the full frequency range amplitude needs to be calculated, ALFF and fALFF are filtered in the opposite order in the frequency domain.The ReHo maps of each voxel was estimated by calculating KCC of the given voxel along with its adjacent 26(3×3×3 − 1) voxels. For Reho, we don't use smoothed data because it affects local spatial properties, so we chose the mReHo value. Significant differences between the baseline and follow-up groups were identified on the ALFF/fALFF values and ReHo maps using pairs t-test (p < 0.05). The results were analyzed by voxelwise family wise error (FWE) correction with a minimum cluster size of at least 5 contiguous voxels. Alternatively, based on the strictness of the group comparison used in this exploratory study, a threshold of p < 0.001 was applied in the conditional group comparison with a minimum cluster size of at least 10 contiguous voxels(uncorrected for multiple comparisons).

Functional Connectivity

At present, seed point based analysis has developed into a major technical means of FC analysis, and the selection of seed points is often the focus of this kind of research. In this way, we choose the seed point method to realize the whole brain analysis. This process uses DPABI software to calculate the functional correlation between the average time series of any two brain regions in different ROI. For the "Define ROI" option, we chose the whole brain mask "HarvardOxford subcort maxprob thr25 2mm"https://neurovault.org/images/1699/, which is a whole-brain mask with 96 nodes. Then, we applied Fisher’s Z transformation to the correlation coefficient matrix(96×96) to improve the normality of the data. We used the two-sample t-test to calculate the intergroup differences in ROI correlation coefficients to compare the FC between the two groups. The threshold of all tests was set at p < 0.05, and false discovery rate(FDR) was used for multiple comparison corrections using the GRETNA software toolbox^[32–33]. The matrix of brain region mapping was also drawn using BrainNet Viewer.

Statistical analysis and visualization

Two-sample t-tests(satisfy normal distribution) and the Mann-Whitney U-test(Non-parametric tests) were used to analyze the group differences in demographics and clinical information. Multivariate linear regression was used to evaluate the neurocognitive test data^[34]. The significance threshold was set as p < 0.05. Statistical tests were performed in SPSS software (version 26.0).The clusters showing significant group differences in ALFF/fALFF and ReHo between the baseline and follow-up groups were visualized with the and DPABI software viewer and SPM12. The registration and display template is "MNI_ICBM152 2009c Nonlinear Asymmetric".We applied Pearson’s correlation analysis to examine the relationship between altered ALFF/ReHo/FC and neurocognitive test scores in the two groups of ANI patients.

Table 1

**Demographic and clinical characteristics of the participants.**The clinical data and neurocognitive test scores between the two groups were compared by a two-sample t-test or Mann–Whitney U test. Significance was set as p < 0.05. a t-test;b Mann–Whitney U Test; and SD-Standard Deviation.
Demographic	Baseline	Follow-up	p value
Age(years)(Mean ± SD)	34.94 ± 7.69	36.2 ± 7.81	0.596^a
Disease course(months)(Mean ± SD)	62.83 ± 33.85	76.45 ± 35.62	＜0.001^a
Prisma viral load(HIV RNA)	TND(49/49)	TND(49/49)
CD4 + T (Mean ± SD)	575.9 ± 295.6	615.3 ± 304.2	＜0.001^a
CD4+/CD8 + ratio (Mean ± SD)	0.63 ± 0.22	0.71 ± 0.23	0.824^a
Neurocognitive performance T-score (Mean ± SD)
Speed of information processing	41.43 ± 6.4	44.97 ± 5.4	0.019^b
Memory(learning and recall)	44.83 ± 7.1	46.24 ± 6.8	0.228^b
Verbal and language	43.26 ± 5.6	48.63 ± 6.5	0.011^b
Abstraction/executive	46.74 ± 6.8	47.82 ± 7.2	0.394^b
Fine motor skills	47.25 ± 7.6	48.03 ± 7.4	0.417^b
Attention/working memory	44.59 ± 6.2	51.17 ± 7.9	＜0.001^b

Participants

The demographics, clinical characteristics, and neurocognitive test results of baseline and follow up are shown in Table 1. There were no significant differences in the demographics between the two groups. In our study, compared with baseline group, follow-up group showed significantly increased T scores in three domains(speed of information processing, verbal and language, attention/working memory).

Differences in ALFF/fALFF

We take a seed-based functional connectivity analysis of rs-fMRI data. A pairs t-test showed significant group differences in ALFF and fALFF between the baseline and follow-up groups. About ALFF, no significant group difference was found using the strict threshold (voxel-level p< 0.001, cluster-level p< 0.05 with FWE correction). With a less strict threshold (voxel-level uncorrected p< 0.001, cluster size > 10), compared with baseline group, the follow-up group demonstrated a significant ALFF decrease in the left middle occipital gyrus (MOG.L), right middle occipital gyrus (MOG.R), right cuneus (CUN.R), left superior frontal gyrus, dorsolateral(SFGdor.L) and right supplementary motor area (SMA.R), as well as ALFF enhancement in the right insula (INS.R)(Figure 1 and Table 2). Similar to ALFF, there are no significant group difference was found using the strict threshold (voxel-level p< 0.001, cluster-level p< 0.05 with FWE correction) in fALFF. With a less strict threshold (voxel-level uncorrected p< 0.001, cluster size > 10), compared with baseline group, the follow-up group had a significant fALFF decrease in the right superior frontal gyrus (SFGdor.R) and SMA.R, as well as fALFF enhancement in the ING.R and right amygdala (AMYG.R)(Figure 2 and Table 3).

Differences in ReHo

We used the same statistical methods described above, but ReHo showed much more robust results.Compared with baseline group, the follow-up group had a significant ReHo decrease in the left median cingulate and paracingulate gyri (DCG.L), right calcarine fissure and surrounding cortex (CAL.R), MOG.R and left precental gyrus(PreCG.L), as well as ReHo enhancement in the left supramarginal gyrus (SMG.L), right postcentral gyrus (PoCG.R), right parahippocampal gyrus(PHG.R) and left calcarine fissure and surrounding cortex (CAL.L)(FWE corrected, voxel-level P < 0.001, cluster-level P < 0.05)(Figure 3 and Table 4).

Difference in FC and correlations of the imaging alterations with clinical variables and neurocognitive function

A two-sample t-test showed significant group differences in FC between the baseline groups and follow-up group. As shown in Figure 4, we believe that FC with p< 0.05 has statistical significance, while there are more FC with significance in the figure, which is difficult to analyze completely. Therefore, in combination with the robust results obtained by ReHo, we find that the matrix diagram shows a statistically significant difference in connection reduction between CAL.L and PreCG.L(coordinate X:Y=13:53/53:13) and between CAL.R and MOG.R(coordinate X:Y=47:79/79:47).

As shown in Figure 5. For follow-up group, increased mean ReHo values in the PreCG.L was positively correlated with the abstraction/executive (r = 0.331, p = 0.020). The increased mean ReHo values in the DCG.L were positively correlated with the age.Significantly increased z values of FC between the MOG. R and CAL.R in follow-up group was also positively correlated with speed of information processing. No statistically significant correlation was found between the parameters of rs-fMRI and clinical indicators in the baseline group, and only the results of FC in the above correlation results passed the multiple comparison correction(FDR＜0.05).

Table 2. Coordinates (X, Y, Z) refer to the peak MNI coordinates of brain regions with peak intensity. A positive t value represents increased ALFF, whereas a negative t value represents decreased ALFF. The significance threshold was set at p < 0.001, cluster＞10. MNI, Montreal Neurological Institute.

Brain regions	Peak MNI, mm			T-score
	X	Y	Z
left middle occipital gyrus	30	-78	18	-3.99
right middle occipital gyrus	45	-69	24	-4.08
right cuneus	6	-78	15	-3.55
left superior frontal gyrus, dorsolateral	-27	-9	69	-3.65
right supplementary motor area	9	12	69	-3.82
right cuneus	48	-6	-3	3.93

Table 3. Coordinates (X, Y, Z) refer to the peak MNI coordinates of brain regions with peak intensity. A positive t value represents increased fALFF, whereas a negative t value represents decreased fALFF. The significance threshold was set at p < 0.001, cluster＞10. MNI, Montreal Neurological Institute.

Brain regions	Peak MNI, mm			T-score
	X	Y	Z
right supplementary motor area	24	15	66	-3.96
right superior frontal gyrus, dorsolateral	21	-3	66	-3.53
right amygdala	15	0	-15	5.51
right lingual gyrus	18	-24	-9	4.79

Table 4. Coordinates (X, Y, Z) refer to the peak MNI coordinates of brain regions with peak intensity. A positive t value represents increased ReHo,whereas a negative t value represents decreased ReHo. The significance threshold was set at p< 0.05(After FWE correction), cluster＞5. MNI, Montreal Neurological Institute.

Brain regions	Peak MNI, mm			T-score
	X	Y	Z
left median cingulate and paracingulate gyri	9	-60	9	-5.72
right calcarine fissure and surrounding cortex	12	-63	9	-5.81
right middle occipital gyrus	48	-72	24	-6.09
left precental gyrus	-39	3	42	-5.61
left supramarginal gyrus	-54	-21	24	7.24
left calcarine fissure and surrounding cortex	30	-39	39	6.70
right postcentral gyrus	-6	-42	39	7.90
right parahippocampal gyrus	12	-21	-24	5.57

The current study that we combined ALFF/fALFF, ReHo, and FC to analyze changes in brain function in patients with ANI in a longitudinal study. Studies have shown that in the context of continuous antiretroviral therapy, abnormal changes in functional networks in some brain regions are still detected, and these changes are associated with some clinical features and cognitive function changes. These findings help elucidate the dynamic changes of brain networks in the ANI group and refine the cognitive control system model.

In our study, compared to baseline group, we found that the mean ReHo values decreased in DCG.L, CAL.R, MOG.R and PreCG.L in follow-up group. The two main methods of rs-fMRI data analysis are functional segregation and functional integration. Functional separation mainly focuses on the function of different brain regions, and ALFF can achieve this function well^[35–36]. Functional integration integrates brain activity into interconnected networks, focusing on interactions between different brain regions. Seed-based and ROI-based FC are important methods for analyzing functional integration. ReHo, on the other hand, is considered to have both functions, with a higher value representing the better consistency between local voxels and neighboring voxels^[38]. Although this may not necessarily explain the significance of local neural activity, it is possible to obtain better explanatory power when multiple resting state indicators with different orientation are used together^[39–40]. Therefore, the relatively robust ReHo results we obtained are valuable for showing the dynamic changes in brain function in ANI patients during follow-up, which is the most significant outcome of this study.

The middle occipital gyrus (MOG), a brain region with intergroup differences, attracted our attention. MOG.R decreased in both ALFF and ReHo values, suggesting that this region may play an important role in the functional changes in ANI patients. It is now clear that the primary visual cortex, located in the occipital lobe, is primarily involved in visual processing and is responsible for analyzing shapes, colors and movements, interpreting and summarizing the images we see^[41]. Incoming visual stimuli are transmitted from the retina to the geniculate nucleus of the lateral thalamus via optic nerve and optic tract, and then via optic radiation to the primary visual cortex of the occipital lobe. MOG is associated with the perception of space around the body, and the fusion of the two information can form stereo vision perception^[42–43]. Previous studies have demonstrated visual associations with changes in occipital lobe function in HIV-infected patients. Wiesman demonstrated using magnetoencephalogram that abnormal occipital dynamics can distinguish cognitive impairment in HIV patients^[44]. Liu's study showed that in HIV patients, the visual cortex was inversely correlated with a decline in the posterior cingulate cortex and left angular gyrus of the default mode network(DMN), and occipital lobe volume was positively correlated with visual-related cognitive function^[45]. The above results are consistent with our finding that the strength of functional connections between MOG.R and CAL.R is positively correlated with the speed of information processing in cognitive tests, and vision plays a crucial role in information processing. This suggests that the decline in our visual-related cognitive functions may be predictive of disease development in ANI patients.

Other brain regions with decreased ReHo values have different functions. Median cingulate and paracingulate gyri(DCG) receive outputs from the amygdala, orbitofrontal gyrus, and medial frontal-gyrus, and transmit impulses to the anterior cingulate gyrus and striatum, which have always been an important part of the emotional circuit. Studies have found that DCG in patients with depression has abnormal cerebral blood flow and metabolism, which suggests that depression may have low DCG function^[46–47]. The calcarine fissure and surrounding cortex (CAL) is the main component of the visual cortex and is a typical sensory granular cortex (Koniocortex cortex). Its input comes mainly from the lateral geniculate part of the thalamus, which receives information from the contralateral visual field^[48]. Some studies have suggested that the smaller visual cortex may allow people to experience hallucinations, and damage to this area has also been reported in HIV-infected patients^{[45, 49]}. The precental gyrus (PreCG) is the motor area of the brain that regulates skeletal muscles throughout the body and controls movement in the contralateral part of the body. It is home to the corticospinal tract and the corticomedulla tract^[50]. There are proprioceptive fibers that also project to the PreCG. Impairment of the motor area appears late in HIV-infected patients, but Li's study of HIV vertically infected adolescent found a correlation between reduced levels of PreCG volume and decreased motor capacity^[51]. Despite the differences in the functions of the three brain regions, the abnormal results in ReHo maps suggest that the patient may have experienced subtle declines in vision, emotional control, and motor functions during ANI, although not yet reflected on the neurocognitive test.

Meanwhile, we also noticed that SMG.L, PoCG.R, PHG.R and CAL.L showed an increase in the value of four brain regions in ReHo map. These four brain regions differ greatly in their functions, processing auditory information, somatosensory, cognitive and emotional control, and visual information processing^[52–53]. This situation is not unique in HIV-infected patients. A longitudinal study of patients with persistent cART found that some brain regions showed increased functional connectivity and even increased gray matter and white matter volume 1–2 years after the acute phase^[54]. The reasons for this are complex, but the most likely explanation is partial immune reconstitution brought about by antiretroviral therapy^[55–56]. The results of neurocognitive test showed that there were no significant differences in motor and memory outcomes between baseline and follow-up groups, suggesting that there might be some functional improvement in the four functional brain areas mentioned above, although this was not fully reflected in correlation tests.

The results of ALFF and fALFF are not completely robust statistically, but they have good stability and repeatability, so they are also valuable in this study. Compared with ALFF, fALFF reflects gray matter signals with more specificity, but less repeatability. Therefore, it is proposed to combine ALFF and fALFF for analysis and research. The main functions of the brain regions with reduced ALFF and fALFF values include basic/advanced visual information processing, self-generated and controlled movement, executive function, and emotional control^[57]. These brain regions have the same function as the parts of the brain where ReHo values decreased, and the rest may have similar function. The amygdala and the right insula showed increased ALFF and fALFF values, which were mainly responsible for emotional reward and punishment mechanism and visceral sensation and movement, respectively^[58].It is worth noting that ALFF and fALFF values in insula increased, which may partly explain why there was no significant change in executive function performance between groups.

At present, seed point based analysis has developed into a major technical means of functional connection analysis, and the selection of seed points is often the focus of this kind of research. But the disadvantage is that it can introduce subjective factors; The results will change according to different seed points, so there is inevitable selection bias^[59–60]. In this study, FC based on the whole brain mask was adopted. Although the data volume was large, all the statistically significant brain regions were analyzed, but the analysis combined with other rs-fMRI results showed that the connection between MOG.R and CAL.R, DCG.L and PreCG.L was reduced. Both of these connections are related to visual pathways and information transduction, on the other hand, providing evidence for the possible decline in vision-related function that we discussed earlier in ANI patients.

Limitation

There are some limitations and shortcomings in this study. First, the use of antiretrovirals was not included as an intra - or inter-group classification condition. The drug use of the participants included in our study is relatively complicated, which may affect the experimental results to some extent. This has been reported in some literatures^[61–62], and we will continue to pay attention to it in the future.

Moreover, although the mask of the whole brain is rich in FC calculation and the bias caused by the subjective selection of seed points is avoided, the selection of different masks will also produce differences in the calculation, which will also affect the connection of different brain regions. In the future, we will also make new ROI on the results we obtained, and conduct research on different network modes and independent component analysis to make the results more perfect.

This study showed that patients with ANI stage may still show statistically significant functional changes in ALFF, ReHo, and FC during treatment. These findings suggest that abnormalities in the cingulate gyrus, peritalate cortex, and anterior central gyrus may indicate disease progression and cognitive decline, while abnormalities in the occipital lobe, visual center, and related functional connections should be of more concern. Of course, the promising results in the amygdala and insula suggest that cognitive function is both declining and improving early in the disease, which is a complex process. These findings help us to better understand the changes of cognitive function and brain network in ANI stage, and provide a favorable basis for further study of the pathophysiological mechanism of HAND.

Ethics Statement and Consent to Participate

The studies involving human participants were reviewed and approved by Beijing You’an Hospital Ethics Committee. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Consent for publication

All authors read and approved the final manuscript.

Authors' Contributions

JM had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy ofthe data analysis and draft of the manuscript. Study concept and design: HL. Acquisition of data: JM, SH, CH, XJ, FX and HL. Analysis and interpretation of data: JM and SH. Critical manuscript revision for important intellectual content: HL and LS. Study conception and design: JL and WW. Statistical analysis: JM and FX. Hongjun Li and Lijun Sun are co-corresponding authors of this paper.

Availability of Data and Materials

The authors confirm that the data supporting the findings of this study are available within the article.

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 82271963 and 61936013]; the Beijing Natural Science Foundation [7212051, L222097]. The funders and sponsors had no role in the study design, data collection, analysis, publication decision, or manuscript preparation.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor JL declared a shared parent affiliation with the authors at the time of review.

Acknowledgments

The authors are grateful to all the subjects who participated in this study and to all the staff members and researchers who collaborated on this study.

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No competing interests reported.

Longitudinal study of functional connectivity changes in asymptomatic neurocognitive impairment(ANI) in HIV- infected patients

Status:

Version 1

Abstract

Figures

Background

Materials and Methods

Participants

Neurocognitive tests

MRI data acquisition

Image pre-processing

Computation of ALFF/fALFF values and ReHo maps

Functional Connectivity

Statistical analysis and visualization

Results

Discussion

Limitation

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1