Depressive symptoms associated with brain morphometry in Mild Cognitive Impairment stage due to Alzheimer ’ s and Parkinson ’ s disease

: Objectives : Although there are studies linking depressive symptoms with psychiatric diseases, and then, with brain morphometry, scarce is the literature examining the association between depressive symptoms and brain morphometry in the prodromal stage of neurodegenerative disorders. In the current analysis


Introduction :
Neurodegenerative diseases are commonly accompanied by depressive symptoms, especially depression and anxiety [1].The presence of depressive symptoms in Alzheimer's dementia (AD) and Parkinson's disease (PD) has a negative impact on the quality of life of both patients and their caregivers [2] and has been also linked with accelerated progression of disability, earlier institutionalization and mortality [3,4].Depressive symptoms of different diseases have been also associated with brain morphometry [5].In general, regarding depression and anxiety disorders, reduced volume of the rostral-dorsal anterior cingulate gyrus appears as a generic effect [6].Although there are studies linking depressive symptoms with psychiatric diseases, and then, with brain morphometry, scarce is the literature examining the association between depressive symptoms and brain morphometry in the context of neurodegenerative disorders.During depressive symptoms, neuroplastic stressrelated processes occur in the hippocampus, cingulum, left amygdala, and right dorsomedial prefrontal cortex [7].Depressive symptoms have been also associated with regional cortical metabolism in AD patients, suggesting that these symptoms are fundamental expressions of the cortical dysfunction of the disease [8].However, reports are not quite linear, with a different study suggesting no association between atrophy of the amygdala and depressive symptoms in AD [9].In a group of anxiety mild PD patients, psychiatric symptom severity was associated with decreased grey matter volume [10], indicating that the precuneus and the anterior cingulate cortex may play a significant role in the pathogenesis of anxiety in PD.By using the Geriatric Depression Scale (GDS), depressive symptoms have also been associated with brain morphometry in PD, and more precisely, with left hippocampal volume, and right parahippocampal gyrus volume [11].However, a different study found no association between apathy and frontotemporal atrophy, in a small group of PD patients; raising questions about which symptom is associated with brain morphometry in this specific neurodegenerative disorder.A different study of patients with essential tremor suggests that symptoms of depression and anxiety could be linked to specific structural brain changes [12].The discrepancy of these results may be explained by the complexity of brain circuits, highlighting the importance of an holistic assessment of brain structure with the stratification of many morphological features.Existing literature points towards a contextspecific association between specific depressive symptoms and brain morphometry measures in neurodegenerative disorders.However, most of the studies examine either AD or PD independently, or in the stage of Mild Cognitive Impairment (MCI), or using a unique brain morphometric factor that cannot provide an accurate delineation of the morphological brain network.Further, most of the studies include quite a small sample size.In the current analysis, we used a fair sample of patients with MCI due to upcoming early AD or PD to examine the association between different depressive symptoms and a large variety of brain morphometric factors.

Methods :
Participants were patients of the Third Age Daycare Center IASIS (http://www.iasisamke.gr/kentro-imeras.html), and all signed an informed consent prior to the evaluation.The diagnosis of the clinical as well as cognitive status of each participant was reached through diagnostic consensus meetings of all the researchers and main investigators, both neurologists and neuropsychologists, with the use of a score higher than 23 in the Mini Mental State Examination (MMSE) as a cutoff, based on existing optimal diagnostic cutoff points [13].Thus, individuals with MCI stage based on AD or PD diagnosis were included in the study.The diagnosis of AD or PD was based on the wellestablished diagnostic criteria [14,15].In both groups, individuals had 11 mean years of education, and mean age of 75 years (see Table 1).All participants answered the Geriatric Depression Scale (GDS), regarding how they felt over the past week of the examination, which is a self-reported measures of depression in older adults, consisted of 30 questions [16].We further created scores reflecting five subscale categories of depression, identified by Adams et al.: dysthymia, withdrawal apathy, anxiety, cognitive concern, and hopelessness [17].All participants were also evaluated on the Addenbrooke's cognitive examination (ACE-R), a test which was designed for dementia screening [18].No further psychiatric evaluation was performed.All patients underwent a magnetic resonance imaging (MRI) exam within a period of two months from the time of assessment.A clinical brain MRI protocol was employed, which included a three-dimensional (3D), high spatial resolution T1-weighted (3D HR-T1) gradient echo pulse sequence for the acquisition of detailed anatomical images.MRI scans were performed in three different diagnostic imaging centers equipped with 6 different MR scanners.Therefore, acquisition parameters varied depending on the MR scanner used.All imaging data were screened by an experienced neuroradiologist (P.T.) for the detection of abnormalities or pathologies and the presence of image artifacts (e.g., due to gross motion).Volumetric segmentation and cortical reconstruction were performed with the FreeSurfer software (http://surfer.nmr.mgh.harvard.edu)(v.6.1)[19][20][21].This was followed by parcellation of the cerebral cortex into units based on gyral and sulca structure, as described at the Destrieux cortical atlas [22].One hundred forty-eight measures (74 from each hemisphere) and 41 regional volumes were generated.We used a PC (i7) Windows 11 for processing the images under NeuroDebian 8.0 cell and Oracle VirualBox 6.1.

Statistical analysis:
Quantitative variables are presented with mean and standard deviation (SD).Qualitative variables are presented with absolute and relative frequencies.For the comparison of proportions chi-square test was used.For the comparison of mean values between study Student's t-test was performed.Odds Ratio (95% Confidence Interval) adjusted for sex, age, years of education, ACE-R and estimated total intracranial volume (eTIV) were used to evaluate the difference of GDS scores between the two study groups.Principal component analysis (PCA) with Varimax rotation was performed in order to reduce variables associated with MRI volumes.The cut-off point for factor loadings was 0.40 and for eigenvalues it was 1.00.Cronbach's a was used to test internal consistency reliability of the factors.Partial correlation coefficients that are based on linear regression analysis were used to explore the association of GDS scores with MRI -components as produced from factor analyses and after controlling for age, educational years, ACE-R and eTIV.All reported p values are two-tailed.Statistical significance was set at p<0.05 and analyses were conducted using SPSS statistical software (version 24.0).7 to 9. Specifically, the third factor (l_factor3) of left hemisphere was positively associated with GDS total scores (p=0.004),Dysthymia (p=0.014),Anxiety (p<0.001), and Hopelessness (p=0.015)(see Table 7).When we examined the right hemisphere, we observed a positive correlation between anxiety symptoms and, the first -(p=0.013),-second (p=0.045), and -fifth factor (p=0.006) (see Table 8).Moreover, the fifth factor (r factor5) of the right hemisphere was marginally associated with GDS total scores (p=0.05).Further, we investigated the association between total and subscale GDS scores with factors of MRI volumes.The analysis revealed a positive association of the second factor (v factor2) and GDS total score (p=0.023),Dysthymia (p=0.022),Anxiety (p=0.005), and Hopelessness (p=0.004)(see Table 9) Moreover, the third factor (v factor3) of MRI volumes was marginally and positevely associated with Anxiety score (p=0.049).To analyze the large MRI data and to.uncover associations between specific brain regions with depressive symptoms, we leveraged the method of PCA [23].PCA allowed us to reduce the multidimensional and interrelated MRI data into uncorrelated variables, named as factors or principal components, that explain the maximum variance of depression symptoms.Each factor is comprised of different and uncorrelated MRI components.The factor loading of each MRI component reflects the correlation coefficient with the given factor, with items having the highest factor loading values to be associated stronger with the factor.Thus, this method allowed us to reduce the dimension of MRI data and identify which brain features are the most discriminating for depressive symptoms among MCI patients.
Our findings suggest that differences in the brain regions of the circular sulcus of the insula and the area of posterior ramus of the lateral sulcus, of both hemispheres, explain most of the variability observed in GDS scores of MCI patients.Patients with major depression disorder (MDD) have been found to exhibit abnormal activity and connectivity of insula primary regions suggesting that this distinct brain area may play an important role in the pathogenesis of depression [24].Thus, our findings are in line with those of previous research, supporting that insula area is affected in patients with depression symptoms.To our knowledge, no other studies until now have associated lateral sulcus regions with depression but given the anatomical adjacency of the two regions; i.e. insula is located deep inside the lateral sulcus [25], one could hypothesize that the functional network of this area is also altered.Based on our findings, anxiety seems to be the only psychiatric symptom being associated with both left and right hemisphere.A wealth of neuroimaging studies have been conducted so far to determine structural and functional brain characteristics for anxiety disorders [26].For instance, a study conducted in children with generalized anxiety disorder reported a higher ratio of gray matter to white matter in the upper temporal lobe of this cohort compared to control [27]; while different effects of distinct brain regions and lateralization in different anxiety disorders are mentioned on a different investigation [26].Such results suggest that anxiety disorders are caused to a certain extent by differential activity in certain prefrontal cortex areas, paving the way to the inclusion of neuroimaging techniques in the diagnostic process of anxiety disorders [26].In the field of neurodegenerative disorders, anxiety has been associated with depression, irritability, aggression, and mania among AD patients, and these aspects have been linked to frontal and prefrontal brain regions of both hemispheres [28].In the current study anxiety was associated with regions of both sides of the brain in accordance with existing literature, indicating the prevailing role of anxiety in neurodegenerative disorders.
There is a high prevalence of dysthymia and major depression among patients with AD [29].
Although some studies suggest that dysthymia might be an emotional reaction to the progressive cognitive decline, and depression might be highly associated with biological factors [29][30][31][32], our results provide evidence that these symptoms can be reflected in brain regions differences among patients with neurodegenerative disorders.This is extremely prominent considering that early detection of depression onset in these patients can facilitate the prompt application of a treatment plan that may delay disease progression.Furthermore, our results are in accordance to previous reports and significantly contribute to a better understanding of psychopathology and the interaction between depressive symptoms and neurodegenerative disorders as illustrated by neuroimaging data [33].The present study is the first to our knowledge which examines a large number of brain measurements in association with both global GDS and specific neuro-depressive symptoms in the MCI stage due to both AD and PD cases.On the other hand, the fact that the depressive symptoms 'evaluation was based on the GDS questionnaire as the only instrument is the main limitation of the study.Depressive symptoms are not just associated with upcoming neurodegenerative disorders; there is now a link with specific brain measurements in MCI due to AD and PD diseases.It remains to be seen if the observed brain alterations are a consequence or a cause of neurodegeneration.Future longitudinal analyses should shed more light on the directionality of the results.Nevertheless, the current study significantly contributes to the field of the clinical application of neuroimaging in the diagnosis of psychiatric disorders that impact MCI patients.Such results could help on the patients 'treatment; probably Cytoskeletal Pathology.Brain Pathol, 2016.26(3): p. 371-86.[32] 32.Matchett, B.J., et al., The mechanistic link between selective vulnerability of the locus coeruleus and neurodegeneration in Alzheimer's disease.Acta Neuropathologica, 2021.141(5): p. 631-650.[33] Levenson, R.W., V.E.Sturm, and C.M. Haase, Emotional and Behavioral Symptoms in Neurodegenerative Disease: A Model for Studying the Neural Bases of Psychopathology.Annual Review of Clinical Psychology, 2014.10(1): p. 581-606.Open Access This article is licensedunder a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.The images or other thirdparty material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.

Table 3 . Odds Ratio for the difference of GDS scores between study groups
*Odds Ratio (95% Confidence Interval) adjusted for sex, age, years of education, ACE-R and eTIV