Mapping the neural substrate of dual-task gait cost in older adults across the cognitive spectrum

doi:10.21203/rs.3.rs-4938981/v1

The dual task cost of gait (DTC) is an accessible and cost-effective test that can help identify individuals with cognitive decline and dementia. However, its neural substrate has not been widely described.

This study aims to investigate the neural substrate of the DTC in older adults across the spectrum of cognitive decline.

A total of 336 individuals from the GAIT study cohort were analyzed, including cognitively healthy (N = 122, 71 ± 3.6 years), those with mild cognitive impairment (N = 168, 71 ± 5.3 years), and those with dementia (N = 46, 80 ± 5.7 years). A DTC of 20% or greater was considered to indicate a high level of slowing down while performing successively two verbal tasks (counting backwards task by ones and naming animals). Voxel-based morphometry was employed to investigate differences in gray matter volume (GMV) between groups, which were dichotomized according to the DTC.

A high DTC in the whole population (N = 336) was associated with a smaller GMV in the bilateral temporal lobe across both dual-task conditions. A moderation analysis was employed to compare the neural substrate between cognitive status groups. This revealed that the dementia group exhibited an additional cluster located in the left precentral gyrus with GMV loss associated with a high naming animals DTC, in contrast to the other cognitive groups.

These results provide new evidence on why dual-task gait capabilities deteriorate in normal and pathological cognitive aging. A clearer understanding of the neural substrate associated with DTC depending on the cognitive status would be valuable to better elucidate this motor marker of dementia.

Dual-task cost of gait

Gray Matter Volume

Neural Substrate

Cognitive Impairment

Aging

The dual-task gait test is a rapid and cost-effective method for assessing the cognitive capabilities of older adults during walking. It can be employed in both clinical and research settings and provides insight into how cognitive resources are used in more natural real-life conditions.(Plummer et Eskes 2015; M. Montero-Odasso et al. 2019) Previous studies have demonstrated that impairment in specific cognitive domains, such as attention, executive function, and working memory, is associated with slower dual-task gait speed.(M. Montero-Odasso et al. 2009) This slowdown in gait or the dual-task gait cost (DTC), while performing a concurrent mental task, is frequently higher in individuals with mild cognitive impairment (MCI) and dementia.(Cullen et al. 2019; Muir et al. 2012) Moreover, a slowing of gait speed by more than 20% when performing a dual task compared to a single task (Sakurai, Bartha, et Montero-Odasso 2019; M. M. Montero-Odasso et al. 2017) is associated with faster progression to dementia in MCI.(M. M. Montero-Odasso et al. 2017) Therefore, the DTC has great potential as a clinical marker of dementia risk. The DTC involves cerebral systems that control both cognitive and gait functions, which may share a common neural substrate.(Yogev-Seligmann, Hausdorff, et Giladi 2008; Al-Yahya et al. 2011)

Despite the growing body of literature supporting the use of DTC, only a few neuroimaging studies have focused on its neural basis with several methodological limitations including sample size, clinical characterization, few regions of interest and neuroimaging metrics extracted.(Sakurai, Bartha, et Montero-Odasso 2019; Subotic et al. 2023; Tripathi, Verghese, et Blumen 2019; Hupfeld et al. 2022) Nevertheless, it is necessary to investigate the underlying pathophysiological processes associated with this parameter and identify the brain areas involved in DTC abnormalities to better understand their full potential for the diagnosis of dementia.(Tian et al. 2023) Previous literature on brain structural analysis and dual-task gait performance has identified an association between regional gray matter volume (GMV) loss and poorer gait, predominantly in the frontal and temporal lobes, the lateral occipital cortex, and the postcentral gyrus. These findings suggest a potential role for these areas in the central control of the cognitive-motor interaction.(Allali et al. 2019; Tripathi, Verghese, et Blumen 2019; Sakurai, Bartha, et Montero-Odasso 2019; Doi et al. 2017) These studies included cohorts of older adults with various stages of cognitive level (from healthy to mild cognitive impairment) without directly questioning the impact of these cognitive statuses on the neural substrate of the dual-task performance.(Koppelmans, Silvester, et Duff 2022) The observed high DTC in individuals with dementia may be caused by the alteration of additional functions necessary to achieve a walk and a cognitive task simultaneously. It is also known that individuals with cognitive decline, have a loss of GMV in several vulnerable regions frequently found abnormally smaller in individuals with dementia. For instance, the most prevalent form of dementia, Alzheimer’s Disease (AD), is characterized by a significant reduction in GMV in the parietal and cingulate cortices, even before the typical clinical manifestations (e.g. memory impairment). In less advanced stages including amnestic MCI (aMCI)(Karas et al. 2004) a significant reduction in GMV in the medial temporal lobe in comparison to healthy cognitively normal individuals can be identified.(Karas et al. 2004; Pennanen et al. 2005) This GMV loss has been correlated with neuropsychological performance in patient with aMCI and AD.(Arlt et al. 2013) Importantly, the aforementioned vulnerable structures could be also involved in dual-task performance.(Subotic et al. 2023; Doi et al. 2017) Conversely, the neural mechanisms underlying gait dysfunction in neurodegenerative disorders remain poorly understood, as evidenced by the literature review conducted by Koppelmans et al.(Koppelmans, Silvester, et Duff 2022) It is, therefore, of great interest to ascertain whether localized GMV loss in dementia and preclinical stages are involved in poorer dual-task gait performance. The dual-task gait performance, expressed in DTC, is of particular interest as it is more closely related to cognitive processing during gait, than either gait or cognitive measures alone.(M. Montero-Odasso et al. 2019) To the best of our knowledge, no studies to date have systematically and comprehensively investigated the neural basis of DTC across the cognitive spectrum.

The objective of the present study was to determine whether there is a neural substrate of the DTC in community-dwelling older adults across the cognitive spectrum and, if such a substrate exists, whether it varies depending on the cognitive status of individuals.

Population:

The participants were recruited from the “Gait and Alzheimer Interactions Tracking” (GAIT) cohort, which conducted a cross-sectional study between November 2009 and November 2015 to compare the gait characteristics of individuals with varying cognitive profiles, ranging from healthy individuals to those with dementia. The study procedure has been described in detail previously.(Beauchet et al. 2019) Participants had to be 60 years of age or older, ambulatory, community-dwelling, with adequate understanding of French language, and without acute medical illnesses in the past month. Additionally, they were required to have a MMSE score of at least > 10, visual acuity of at least ≥ 2/10, no severe depressive symptoms (15-item Geriatric Depression Scale score ≤ 10), and no acute medical illnesses in the past month. Individuals were excluded from participation if they had preexisting locomotor disorders, a history of stroke or sensorimotor sequelae from the central nervous system, any acute medical or surgical condition less than three months old, a score of greater than 10 on the 15-item Geriatric Depression Scale, or if they were unable to walk unaided for less than 15 minutes. For this analysis, we only included participants who underwent a brain MRI and a dual-task gait assessment. Participants were included after providing written informed consent to participate in the study. The research protocol was approved by the Angers Ethics Committee (CPP Ouest II − 2009–12) and registered as ClinicalTrials.gov n° NCT01315717.

Clinical assessment:

Cognitive assessment:

The Memory clinic of Angers University Hospital (France) assessed all participants. Cognitive status diagnoses were made during multidisciplinary assessments involving geriatricians, neurologists, and neuropsychologists. Controls were subjects with normal neuropsychological results (MMSE score > 25 and negative Winblad criteria).(Winblad et al. 2004) Dementia was diagnosed using the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision)(Quinn 1999) and NINCDS/ADRDA criteria.(Bruno Dubois et al. 2007) MCI was diagnosed according to the criteria detailed by Winblad et al.(Winblad et al. 2004) These criteria require that the person is neither normal nor with dementia, there is evidence of cognitive deterioration shown by either objectively measured decline over time and/or subjective report of decline by self and/or informant in conjunction with objective cognitive deficits, and activities of daily living are preserved, and complex instrumental functions are either intact or minimally impaired.

Participants underwent evaluation using a neuropsychological battery that included standardized assessments of various cognitive domains, as previously described.(Beauchet et al. 2019) Global cognition was assessed using the Mini-Mental State Examination (MMSE),(Folstein, Folstein, et McHugh 1975) Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog),(Kueper, Speechley, et Montero-Odasso, s. d.) and Frontal Assessment Battery (FAB).(B. Dubois et al. 2000)

Gait evaluation:

Gait assessment was conducted using a Gaitrite© System, which is 972 cm long, to evaluate spatiotemporal gait parameters in older adults according to the European guidelines.(European GAITRite® Network Group, Kressig, et Beauchet 2006) Participants completed three gait trials in random order to minimize the effects of learning and fatigue. They were instructed to walk at their usual gait speed (cm/s) while wearing their own footwear. During the dual-task trials, participants were instructed to walk at their usual pace without prioritizing either the gait or cognitive task. They were asked to perform the following cognitive tasks aloud: (i) counting backwards (CB) from fifty to zero one by one and (ii) naming animals (NA). The dual-task gait cost (DTC) was calculated for each trial using the appropriate gait speed with the formula: DTC = [(single-task gait speed - dual-task gait speed) / single-task gait speed] x 100.(Plummer et Eskes 2015) DTC was expressed as a percentage of slowing from the usual gait speed due to the added cognitive task. High DTC was defined as a slowing down of 20% or more, while low DTC is defined as less than 20%, as determined by our previous work.(Sakurai, Bartha, et Montero-Odasso 2019; M. M. Montero-Odasso et al. 2017)

MRI acquisition:

Brain imaging was performed using either a 1.5-Tesla MRI scanner (until 2011, Magnetom Avanto; Siemens, Erlangen, Germany) or a 3-Tesla (from 2011, Magnetom Skyra, Siemens, Erlangen, Germany) using a standard MRI protocol including 3D T1-weighted magnetization prepared rapid acquisition gradient echo (MP-RAGE) images (for 1.5-Tesla MRI: acquisition matrix = 256×256×144, field of view (FOV) = 240×240×187 mm, echo time (TE)/repetition time (TR)/inversion time (TI) = 40.07 ms/2170 ms/1100 ms) ; for 3-Tesla MRI acquisition matrix = 256x256x128, FOV = 240x240x176 mm TE/TR/TI = 2.98 ms/2300 ms/900 ms).

MRI processing:

The analysis utilized the CAT12 toolbox (Computational Anatomy Toolbox 12, vCAT12.8.2), an extension of SPM12 (Statistical Parametric Mapping, v7771), to conduct the voxel-based morphometry (VBM) on anatomical T1-weighted images. These images underwent automated segmentation into gray matter, white matter, and cerebrospinal fluid. Additionally, the volume of white matter hyperintensities (WMHs) was delineated as a separate class using the “expert mode”.(Gaser et al. 2022) The pre-processing steps, adhering to the standard VBM protocol, involved comprehensive brain tissue segmentation and spatial alignment using the Shooting template.(Ashburner et Friston 2000; 2011; Gaser et al. 2022) The resulting gray matter volume (GMV) images, aligned to the template, were of a resolution of 1.5x1.5x1.5. Quality assurance was robust, involving both automated and visual inspections to ensure the absence of artifacts. To minimize individual gyral variations, the data were smoothed using a 6 mm full width at half maximum (FWHM) Gaussian Kernel. An absolute gray matter threshold of 0.1 was applied to ensure the inclusion of solely gray matter regions in the statistical analyses.

Statistical analysis was conducted using a whole-brain ANOVA that included covariates such as age, sex, educational level (post-secondary or none), MRI field strength (1.5T or 3T), WMHs, and total intracranial volume (TIV). The VBM results were adjusted for multiple comparisons using the family-wise error (FWE) method, with significance set at p < 0.05. This correction was implemented alongside threshold-free cluster enhancement (TFCE) with 5,000 permutations.(Spisák et al. 2019) Significant clusters were identified on VBM statistical maps. These clusters were defined as having notable differences in gray matter (negative or positive) and comprising at least 10 contiguous voxels. The locations of these clusters were determined using the Automated Anatomical Labeling version 3 (AAL3) atlas.(Rolls et al. 2020)

To extract GMV data from each significant cluster, binary masks were applied to the non-smoothed, modulated GMV images of each participant.

Statistics:

To describe the study population based on cognitive status groups (control, individuals with MCI, and those with dementia), we utilized appropriate statistical tests such ANOVA with post-hoc test and Tukey correction, or Chi-square test as appropriate. To adjust for skewness, we log-transformed WMHs volumes. We conducted statistical comparisons using JASP (Version .17.1, https://jasp-stats.org/).

To compare GMV (outcome) between the high and low DTC groups (predictor), we used the general linear model approach implemented in SPM12 described above.

To investigate the effect of cognitive status on the relationship between GMV (outcome) and high versus low DTC (predictor), a moderation analysis was conducted using the PROCESS macro on R Core Team (Version 4.2.3, 2023, https://www.R-project.org/) and following Hayes’ guidelines. The PROCESS macro provides results that include logistic regression and the interaction with the regression coefficient if the interaction is significant.

To probe for the moderation effect of the cognitive status (H1), it was necessary for the relationships for (i), (ii), and (iii) to be significant (Fig. 1). These relationships include (i) the direct effect of the predictor (DTC) on GMV, (ii) the direct effect of the moderator (cognitive status) on GMV, and (iii) the direct interaction effect (DTC x cognitive status) on GMV. In R PROCESS, the software automatically calculates the interaction effect and produces the likelihood ratio test explained by the moderating effect of cognitive status (increase due to interaction). The potential moderators, each assessed within an independent moderation model, were as follows: dementia versus control groups, MCI versus control groups, dementia versus MCI groups. A p < 0.05 was considered statistically significant. The moderation model was adjusted for covariates including age, sex, educational level, TIV, and MMSE.

A total of 336 participants were included in the current analysis, consisting of 122 control participants, 168 participants with MCI, and 46 participants with dementia. 3 participants (1 healthy control and 2 people with dementia) did not perform the NA gait task.

Cohort characteristics:

Table 1 summarizes the main demographic characteristics. The dementia group was significantly older (79 ± 5.7 years) than the control (71 ± 3.6, p < 0.001) and MCI (73 ± 5.3, p < 0.001) groups and the MCI group was older than the control group (p = 0.02). Additionally, the dementia group had a higher proportion of women compared to the control group (p < 0.001). The MCI group had a higher body mass index (27 ± 4) than the control group (25 ± 3, p = 0.02). There were no differences in comorbidities between the groups.

Among the participants in the MCI group, 40 individuals (24%) exhibited amnestic features, while 110 individuals (65%) displayed non-amnestic symptoms. Additionally, 18 individuals (11%) showed impairments across multiple cognitive domains. Of those diagnosed with dementia, 37 individuals (80%) were diagnosed with Alzheimer's Disease (AD), while 9 individuals (20%) had a mixed form of dementia involving vascular and other neurodegenerative factors, excluding AD.

After adjusting for sex, no significant differences in TIV were found among the three groups (p = 0.10, Table 1). Participants with dementia had a higher volume of WMHs than the control participants (12.2 ± 15.9 mL versus 3.1 ± 30.0 mL, p < 0.001, Table 1) and people with MCI (5.5 ± 7.3 mL, p < 0.001, Table 1), and people with MCI had a higher volume of WMHs than the control group (p < 0.001, Table 1).

As expected, the MMSE and ADAS-Cog scores were significantly lower in the dementia group compared to the control and MCI groups (p < 0.001, Table 1). Participants with a cognitive impairment (including those with MCI and dementia status) exhibit significantly lower dual-task gait speed and higher DTC (indicating worse performance) across all conditions (Table 1). There was no significant difference of DTC between individual with MCI and those with dementia (unadjusted and adjusted from age, sex, educational level and MMSE).

Brain gray matter volume (GMV) difference following the load of dual task cost (DTC):

Among the entire population, individuals with a higher counting backward dual-task cost (CB DTC) exhibited a smaller GMV compared to those with a low CB DTC. This was observed in five clusters located in the left and right temporal lobes (1.2 ± 0.2 versus 1.1 ± 0.2 mL for the cluster 1, see Table 2 and Fig. 2).

The study found that individuals with a high cost for naming animals (NA DTC) while performing a dual-task had lower GMV compared to those with a low NA DTC in 11 clusters. These clusters were in the left hemisphere in the lingual, cuneus, angular, precentral, and superior occipital gyri, and in the right hemisphere in the inferior temporal, superior and medio-orbital frontal gyri, and cerebellum (Table 2 and Fig. 2).

Additionally, Fig. 2 shows clusters (in yellow) that overlap in both dual-task conditions, located in the temporal lobe bilaterally.

There was no difference in WMHs volume according to the load of the DTC (CB and NA condition).

Moderation analysis:

Three models of moderation analysis were conducted to investigate whether the cognitive status of the subjects moderated the association between the DTC and GMV clusters.

The relationship between the CB DTC and GMV clusters was not found to be moderated by differences between the cognitive groups (including dementia versus control groups, MCI versus control groups, dementia versus MCI groups).

However, the association between NA DTC and GMV was moderated by cognitive status, with significant differences observed between subjects with dementia (overall: b= -0.07, p = 0.03) versus controls subjects in a cluster located in the left precentral gyrus (Table 3 and Fig. 3). In the dementia group, lower GMV in the left precentral gyrus was associated with high NA DTC (conditional effect, b=-0.18, p = 0.002), while this relationship was not significant in the control group (conditional effect, b=-0.03, p = 0.47).

Additionally, the cognitive status comparing people with dementia versus MCI group significantly moderated the association between NA DTC and GMV loss in the left precentral gyrus (overall: b= -0.17, p = 0.01, conditional effect for dementia group: b=-0.18, p = 0.002 versus for MCI group conditional effect: b=-0.01, p = 0.68) (Table 3 and Fig. 3).

No other moderation effect was found between groups (i.e. dementia group versus control group nor MCI versus control group nor MCI versus dementia group) in the other clusters tested (Table 3 and Fig. 3).

This study aims to explore the neural substrate of DTC across the cognitive spectrum. We found that older adults with a high DTC had smaller GMV in the bilateral middle and inferior temporal gyri. Additionally, when individuals were separated according to their cognitive status, it was observed that those with dementia exhibited an additional cluster where high NA DTC was associated with smaller GMV in the left precentral gyrus. This association was not observed in other cognitive groups, including the MCI and control groups.

In the entire cohort of older adults, poorer dual-task performance is significantly associated with smaller GMV in the anterior part of the lateral temporal lobes (middle and inferior gyri). The anterior part of the middle and inferior temporal gyri play a critical role in several cognitive processes, including semantic visual recognition, verbal semantic memory and language.(Herlin, Navarro, et Dupont 2021) They are also essential for integrating sensory information and facilitating complex motor control.(Rosso et al. 2013) Smaller GMV in these areas may indicate difficulties to handle the cognitive task while maintaining motor control, resulting in a high DTC.

Our study identified that NA DTC can be an index of extensive GMV loss in key brain areas related to motor and high-level cognitive dysfunctions, specifically in more advanced stages of cognitive decline. This is consistent with the naming animals task having a broader cortical demand, involving additional cognitive abilities beyond a simple backward counting by ones task.(Beauchet et al. 2005) The smaller GMV in the right superior frontal and medio-orbito frontal gyri associated with high NA DTC, which play a role in executive functions, illustrates the known contribution of this region to dual-task gait alteration.(Al-Yahya et al. 2011) The loss of GMV in the cerebellum associated with high NA DTC also plays a role in the coordination of complex motor control. These findings are consistent with those reported in previous studies, which have demonstrated that the cognitive-motor interaction involves specific functions, including attention, executive function, and coordination(Yogev-Seligmann, Hausdorff, et Giladi 2008; Al-Yahya et al. 2011). Additionally, they provide evidence regarding the neural substrate associated with this phenomenon.

In individuals with dementia, the study found that the left precentral gyrus, also known as the primary motor cortex (M1) is linked to high NA DTC, in contrast with other cognitive groups. This suggests that in people with dementia, the GMV loss in the M1, the region that initially serves as an effector of central motor command, is partially responsible for the observed impairment in dual-task gait. This supplementary affected region in the dementia group may elucidate why those individuals are more prone to experience high DTC as a consequence of the damage to neuronal integrity in a crucial area implicated in the central motor control. Therefore, individuals with dementia must manage their remaining intact brain resources and prioritize between motor and cognitive tasks for action execution, which frequently conduct to poorer dual-task performance.

In individuals with MCI, the neural substrate of high NA DTC is identical to that observed in the control group, affecting all aforementioned areas with the exception of the left precentral gyrus. However, individuals with MCI have higher clinical dual-task gait and cognitive impairments in comparison to the control group. This can be explained by the temporal sequence of neurodegeneration, whereby alterations in clinical gait performance precede structural alterations to the brain.(Tian et al. 2023) This illustrates the interest of DTC as a clinical early marker of cognitive decline.

In contrast to some other studies, we did not find an association between dual-task performance and the volume of WMHs. This association remains controversial in the literature. (Subotic et al. 2023; Nadkarni et al. 2012; Blumen, Jayakody, et Verghese 2023) Moreover, the method used in the current study to quantify these parameters was not specific enough because it only encompassed the whole brain, while associations were reported in specific regions of interest, such as the frontotemporal region. Additionally, the T1 was not as specific as a FLAIR and may have underestimated the volume of WMHs.(Gaser et al. 2022) Furthermore, some authors attribute the burden of vascular abnormalities to be more linked to the motoric cognitive risk syndrome, which is another subgroup at high risk of dementia progression.(Gomez et al. 2022) However, this subgroup was not considered in the current analysis.

This study contributes to the understanding of the underlying pathophysiological mechanisms of altered cognitive-motor interaction across the spectrum of cognitive decline in older adults, supported by a large sample size and a well-characterized population. However, several limitations must also be considered. First, the cross-sectional design does not permit causal conclusions. Second, all subjects were recruited from a memory clinic and some participants in the control group had subjective cognitive complaints. Moreover, other gait parameters such as gait variability,(Pieruccini-Faria et al. 2021) as well as combined marker like genomic analysis(Sakurai et al., s. d.) should be considered for further analysis. Analyzing the cognitive load by accounting for the number of errors during the cognitive task would also be insightful.(Goh, Pearce, et Vas 2021) Finally, the use of two different field strengths in MRI (1.5 and 3T), although adjusted for as a covariate, is another limitation. Nevertheless, the processing software used has been shown to be reliable across scanners and, the robustness of automated methods for brain volume measurement between 1.5 and 3 Tesla scanners has been previously established.(Gaser et al. 2022; Heinen et al. 2016)

The present study found an association between high DTC and reduced gray matter volume in bilateral temporal, left parieto-occipital, and right frontal areas in a cohort of older adults. Importantly, the smaller GMV in the left M1 associated with high DTC are only found in the dementia group and may be explain why these subjects experience more frequently dual-task gait disturbances. These findings advance current knowledge in the neural substrate of cognitive-motor interaction, particularly in the context of dementia, and could be used as an uncostly test to early detect and monitor dementia progression. Future research directions may involve conducting longitudinal studies to validate these cross-sectional associations.

SD: standard deviation

n, number of participants

*AD = Alzheimer’s Disease, Mixed = mixed form of dementia

† MMSE = Folstein Mini

mental State Examination (scores range from 0 to 30, higher scores representing better function),

‡ ADAS

Cog = Alzheimer’s Disease Assessment Scale (score ranges from 0 to 70 with higher scores suggesting greater impairment, 59 missing data),

CB = counting backward, NA = naming animals

P

value significant (i.e,<0.05) in bold.

MCI, mild cognitive impairment

Acknowledgments:

The authors acknowledge the following teams for their contributions to this article:

− The staff from Angers University Memory Clinic (in particular J. Gautier), France, for daily assistance. There was no compensation for this contribution.

− The team from the Department of Radiology, Angers University Hospital, France, for technical support. There was no compensation for this contribution.

Source of Funding:

The first author was supported by a grant from the French Society of Physical and Rehabilitation Medicine (SOFMER). The study was financially supported by the French Ministry of Health (Projet Hospitalier de Recherche Clinique national n° 2009-A00533-54). The authors thank this institution for its support. This study has been registered in ClinicalTrials n° NCT01315717.

Disclosure: The authors have no conflicts of interest to declare.

Author contributions:

CA and ML was responsible for data acquisition. PA and MD were responsible for data analysis; this was overseen by CA, MD and RB. ML, FPF, MMO were involved in drafting the manuscript. All authors contributed to reviewing and editing the manuscript.

Consent Statement: All participants provided informed consent.

Allali G, Montembeault M, Saj A, Wong CH, Olivier Beauchet (2019) Liam Anders Cooper-Brown, Louis Bherer, et. « Structural Brain Volume Covariance Associated with Gait Speed in Patients with Amnestic and Non-Amnestic Mild Cognitive Impairment: A Double Dissociation ». Édité par Manuel Montero-Odasso et George Perry. Journal of Alzheimer’s Disease 71 (s1): S29–39. https://doi.org/10.3233/JAD-190038
Al-Yahya E, Dawes H, Smith L, Dennis A, Howells K, Janet Cockburn (2011) « Cognitive Motor Interference While Walking: A Systematic Review and Meta-Analysis ». Neurosci Biobehav Rev 35(3):715–728. https://doi.org/10.1016/j.neubiorev.2010.08.008
Arlt Sönke, Buchert R, Spies L, Eichenlaub M, Lehmbeck JT, et, Holger Jahn (2013) « Association between Fully Automated MRI-Based Volumetry of Different Brain Regions and Neuropsychological Test Performance in Patients with Amnestic Mild Cognitive Impairment and Alzheimer’s Disease ». Eur Arch Psychiatry Clin NeuroSci 263(4):335–344. https://doi.org/10.1007/s00406-012-0350-7
Ashburner J, et, Friston KJ (2000) « Voxel-Based Morphometry—The Methods ». NeuroImage 11 (6): 805–21. https://doi.org/10.1006/nimg.2000.0582. 2011. « Diffeomorphic Registration Using Geodesic Shooting and Gauss–Newton Optimisation ». NeuroImage 55 (3): 954–67. https://doi.org/10.1016/j.neuroimage.2010.12.049
Beauchet O, Dubost Véronique, Régis Gonthier, et, Reto W, Kressig (2005) « Dual-Task-Related Gait Changes in Transitionally Frail Older Adults: The Type of the Walking-Associated Cognitive Task Matters ». Gerontology 51 (1): 48–52. https://doi.org/10.1159/000081435
Beauchet O, Launay CP, Sekhon H, Montembeault M, Gilles Allali (2019) « Association of Hippocampal Volume with Gait Variability in Pre-Dementia and Dementia Stages of Alzheimer Disease: Results from a Cross-Sectional Study ». Exp Gerontol 115(janvier):55–61. https://doi.org/10.1016/j.exger.2018.11.010
Blumen HM, Jayakody O, Joe Verghese (2023) « Gait in Cerebral Small Vessel Disease, Pre-Dementia, and Dementia: A Systematic Review ». Int J Stroke: Official J Int Stroke Soc 18(1):53–61. https://doi.org/10.1177/17474930221114562
Cullen S, Borrie M, Carroll S, Sarquis-Adamson Y, Pieruccini-Faria F, McKay S, et Manuel Montero-Odasso (2019) « Are Cognitive Subtypes Associated with Dual-Task Gait Performance in a Clinical Setting? » Édité par Manuel Montero-Odasso et George Perry. J Alzheimer’s Disease 71(s1):S57–64. https://doi.org/10.3233/JAD-181196
Doi T, Blumen HM, Verghese J, Shimada H, Makizako H, Tsutsumimoto K, Hotta R, Nakakubo S, Takao Suzuki (2017) « Gray Matter Volume and Dual-Task Gait Performance in Mild Cognitive Impairment ». Brain Imaging Behav 11(3):887–898. https://doi.org/10.1007/s11682-016-9562-1
Dubois B, Slachevsky A, Litvan I, Pillon etB (2000) « The FAB: A Frontal Assessment Battery at Bedside ». Neurology 55(11):1621–1626. https://doi.org/10.1212/wnl.55.11.1621
Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, Delacourte André et al (2007) « Research Criteria for the Diagnosis of Alzheimer’s Disease: Revising the NINCDS-ADRDA Criteria ». Lancet Neurol 6(8):734–746. https://doi.org/10.1016/S1474-4422(07)70178-3
Olivier Beauchet, Kressig RW (2006) « Guidelines for Clinical Applications of Spatio-Temporal Gait Analysis in Older Adults ». Aging Clin Exp Res 18(2):174–176. https://doi.org/10.1007/BF03327437
Folstein MF, Folstein SE, et, McHugh PR (1975) « Mini-Mental State. A Practical Method for Grading the Cognitive State of Patients for the Clinician ». J Psychiatr Res 12(3):189–198. https://doi.org/10.1016/0022-3956(75)90026-6
Gaser C, Dahnke R, Thompson PM, Kurth F, Luders E (2022) « CAT – A Computational Anatomy Toolbox for the Analysis of Structural MRI Data ». https://doi.org/10.1101/2022.06.11.495736. Alzheimer’s Disease Neuroimaging InitiativePreprint. Neuroscience
Goh H-T, Pearce M, Asha Vas (2021) « Task Matters: An Investigation on the Effect of Different Secondary Tasks on Dual-Task Gait in Older Adults ». BMC Geriatr 21(1):510. https://doi.org/10.1186/s12877-021-02464-8
Gomez GT, Rebecca F, Gottesman KP, Gabriel P, Palta AL, Gross A, Soldan MS, Albert et al (2022) « The Association of Motoric Cognitive Risk with Incident Dementia and Neuroimaging Characteristics: The Atherosclerosis Risk in Communities Study ». Alzheimer’s Dement 18(3):434–444. https://doi.org/10.1002/alz.12412
Heinen R, Bouvy WH, Mendrik AM, Viergever MA, Biessels GJ, Jeroen de Bresser (2016) « Robustness of Automated Methods for Brain Volume Measurements across Different MRI Field Strengths ». PLoS ONE 11(10):e0165719. https://doi.org/10.1371/journal.pone.0165719
Herlin B, Navarro V, et Sophie Dupont (2021). « The Temporal Pole: From Anatomy to Function-A Literature Appraisal ». Journal of Chemical Neuroanatomy 113 (avril):101925. https://doi.org/10.1016/j.jchemneu.2021.101925
Hupfeld KE, Justin M, Geraghty, Heather R, McGregor CJ, Hass O, Pasternak D, Seidler (2022) « Differential Relationships Between Brain Structure and Dual Task Walking in Young and Older Adults ». Front Aging Neurosci 14(mars):809281. https://doi.org/10.3389/fnagi.2022.809281
Karas GB, Scheltens P, Rombouts SaRB, Visser PJ, van Schijndel RA, Fox NC, et, Barkhof F (2004) « Global and Local Gray Matter Loss in Mild Cognitive Impairment and Alzheimer’s Disease ». NeuroImage 23 (2): 708–16. https://doi.org/10.1016/j.neuroimage.2004.07.006
Koppelmans V, Silvester B, Kevin Duff (2022) « Neural Mechanisms of Motor Dysfunction in Mild Cognitive Impairment and Alzheimer’s Disease: A Systematic Review ». J Alzheimer’s Disease Rep 6(1):307–344. https://doi.org/10.3233/ADR-210065
Kueper JK, Speechley M et Manuel Montero-Odasso. s. d. « The Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog): Modifications and Responsiveness in Pre-Dementia Populations. A Narrative Review ». J Alzheimer’s Disease 63 (2): 423–444. https://doi.org/10.3233/JAD-170991
Montero-Odasso, Manuel QJ, Almeida L, Bherer AM, Burhan R, Camicioli J, Doyon S, Fraser et al (2019) « Consensus on Shared Measures of Mobility and Cognition: From the Canadian Consortium on Neurodegeneration in Aging (CCNA) ». Journals Gerontology: Ser A 74(6):897–909. https://doi.org/10.1093/gerona/gly148
Montero-Odasso, Manuel H, Bergman NA, Phillips, Chek H, Wong N, Sourial, Howard Chertkow (2009) « Dual-Tasking and Gait in People with Mild Cognitive Impairment. The Effect of Working Memory ». BMC Geriatr 9:41. https://doi.org/10.1186/1471-2318-9-41
Montero-Odasso, Manuel M, Sarquis-Adamson Y, Speechley M, Borrie MJ, Hachinski VC, Wells J, Riccio PM et al (2017) « Association of Dual-Task Gait With Incident Dementia in Mild Cognitive Impairment: Results From the Gait and Brain Study ». JAMA Neurol 74(7):857. https://doi.org/10.1001/jamaneurol.2017.0643
Muir SW, Speechley M, Wells J, Borrie M, Gopaul K, Manuel Montero-Odasso (2012) « Gait Assessment in Mild Cognitive Impairment and Alzheimer’s Disease: The Effect of Dual-Task Challenges across the Cognitive Spectrum ». Gait Posture 35(1):96–100. https://doi.org/10.1016/j.gaitpost.2011.08.014
Nadkarni NK, Levine B, McIlroy WE, et, Black SE (2012) « Impact of Subcortical Hyperintensities on Dual-Tasking in Alzheimer Disease and Aging ». Alzheimer Dis Assoc Disord 26(1):28–35. https://doi.org/10.1097/WAD.0b013e3182172c58
Pennanen C, Testa C, Laakso M, Hallikainen M, Helkala E, Hanninen T, Kivipelto M et al (2005) « A voxel based morphometry study on mild cognitive impairment ». J Neurol Neurosurg Psychiatry 76(1):11–14. https://doi.org/10.1136/jnnp.2004.035600
Pieruccini-Faria F, Black SE, Masellis M, Smith EE, Quincy J, Almeida, Karen ZH, Li L, Bherer R, Camicioli, Manuel Montero‐Odasso (2021) « Gait Variability across Neurodegenerative and Cognitive Disorders: Results from the Canadian Consortium of Neurodegeneration in Aging (CCNA) and the Gait and Brain Study ». Alzheimer’s Dement 17(8):1317–1328. https://doi.org/10.1002/alz.12298
Plummer P, et, Gail Eskes (2015) « Measuring treatment effects on dual-task performance: a framework for research and clinical practice ». Frontiers in Human Neuroscience 9 (avril):225. https://doi.org/10.3389/fnhum.2015.00225
Quinn BP (1999) « Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Primary Care Version ». Primary Care Companion to The Journal of Clinical Psychiatry 1 (2): 54–55
Rolls ET, Huang C-C, Lin C-P, Feng J, et Marc Joliot (2020). « Automated Anatomical Labelling Atlas 3 ». NeuroImage 206 (février):116189. https://doi.org/10.1016/j.neuroimage.2019.116189
Rosso AL, Stephanie A, Studenski, Wen G, Chen, Howard J, Aizenstein NB, Alexander DA, Bennett SE, Black et al (2013) « Aging, the Central Nervous System, and Mobility ». Journals Gerontol Ser A: Biol Sci Med Sci 68(11):1379–1386. https://doi.org/10.1093/gerona/glt089
Sakurai R, Bartha R, Manuel Montero-Odasso (2019) « Entorhinal Cortex Volume Is Associated With Dual-Task Gait Cost Among Older Adults With MCI: Results From the Gait and Brain Study ». Journals Gerontology: Ser A 74(5):698–704. https://doi.org/10.1093/gerona/gly084
Sakurai R, Cornish FP-FB, Fraser J, Binns MA, Beaton D, Dilliott AA et al s. d. « Link among Apolipoprotein E E4, Gait, and Cognition in Neurodegenerative Diseases: ONDRI Study ». Alzheimer’s & Dementia n/a (n/a). Consulté le 15 avril 2024. https://doi.org/10.1002/alz.13740
Spisák Tamás, Spisák Zsófia, Zunhammer M, Bingel U, Smith S, Nichols T (2019) et Tamás Kincses. « Probabilistic TFCE: A Generalized Combination of Cluster Size and Voxel Intensity to Increase Statistical Power ». NeuroImage 185 (janvier):12–26. https://doi.org/10.1016/j.neuroimage.2018.09.078
Subotic A, Gee M, Nelles K, Ba F, Dadar M, Duchesne S, Sharma B et al (2023) « Gray Matter Loss Relates to Dual Task Gait in Lewy Body Disorders and Aging ». Journal of Neurology, octobre. https://doi.org/10.1007/s00415-023-12052-y
Tian Q, Montero-Odasso M, Buchman AS, Mielke MM, Espinoza S, DeCarli CS, Newman AB et al (2023) « Dual Cognitive and Mobility Impairments and Future Dementia - Setting a Research Agenda ». Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association 19 (4): 1579–86. https://doi.org/10.1002/alz.12905
Tripathi S, Verghese J, et, Helena M, Blumen (2019) « Gray Matter Volume Covariance Networks Associated with Dual-Task Cost during Walking-While-Talking ». Hum Brain Mapp 40(7):2229–2240. https://doi.org/10.1002/hbm.24520
Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund L-O, Nordberg A et al (2004) « Mild Cognitive Impairment–beyond Controversies, towards a Consensus: Report of the International Working Group on Mild Cognitive Impairment ». J Intern Med 256(3):240–246. https://doi.org/10.1111/j.1365-2796.2004.01380.x
Yogev-Seligmann, Galit JM, Hausdorff, Nir Giladi (2008) « The Role of Executive Function and Attention in Gait ». Mov Disorders: Official J Mov Disorder Soc 23(3):329–342 quiz 472. https://doi.org/10.1002/mds.21720

Table 1. Demographic Characteristics of the Participants According to Cognitive Status:

					Post hoc analysis
Total population (n = 336)	Control Group (n= 122)	MCI (n=168)	Dementia (n=46)	p value	Control versus MCI	Control versus Dementia	MCI versus Dementia
Age (years), (mean±SD)	71 ± 3.6	73 ± 5.3	79 ± 5.7	<0.001	0.02	<0.001	<0.001
Women, n (%)	57 (47%)	60 (36%)	31 (66%)	<0.001	0.18	0.07	<0.001
Post secondary education, n (%)	80 (66%)	94 (56%)	15 (32%)	<0.001	0.10	<0.001	0.003
Body mass index (kg/cm²) (mean ±SD)	25 ± 3	27 ± 4	26 ± 4	0.03	0.02	0.75	0.46
Number of comorbidities (mean±SD)	2 ± 1.5	2 ± 1.7	3 ± 2.3	0.14	0.36	0.15	0.60
Cognitive Profile n (%)	NA	Amnestic : 40 (24%) Nonamnestic : 110 (65%) Multidomain : 18 (11%)	AD*: 37 (80%) Mixed : 9 (20%)	NA	NA	NA	NA
Total intracranial volume (mL) adjusted on sex, (mean ±SD)	1480±132	1524±139	1475±137	0.10	0.12	0.27	0.98
Volume of white matter hyperintensities (mL)	3.1± 30.0	5.5±7.2	12.2±15.9	<0.001	<0.001	<0.001	<0.001
	Global cognition
MMSE score † (mean±SD)	28 ± 1.3	27 ± 1.9	24 ± 4.5	<0.001	<0.001	<0.001	<0.001
ADAS-Cog score ‡ (mean±SD)	3.9 ± 1.5	5.7 ± 2.1	15 ± 6.6	<0.001	<0.001	<0.001	<0.001
	Gait speed
Single gait (cm/s), (mean±SD)	112± 19	106±19	80±23	<0.001	0.031	<0.001	<0.001
Counting backward (cm/s), (mean ±SD)	114±23	103±24	75±27	<0.001	<0.001	<0.001	<0.001
Naming animals (cm/s), (mean ±SD)	100±25	87±27	61±25	<0.001	<0.001	<0.001	<0.001
	High dual-task cost (>20%)
Counting Backward	6 (5%)	26 (15%)	10 (22%)	0.003	0.02	0.01	0.77
Naming animals	34 (28%)	74 (44%)	26 (59%)	<0.001	0.02	0.001	.21

NOTE. Where appropriate the mean or median is shown with standard deviation in parentheses.

Comparison based on one-way ANOVA and post hoc test with Tukey correction or Chi-square test as appropriate.

P-value significant (i.e,<0.05) in bold.

1individual Control and 2 participants with dementia didn’t perform the naming animals task.

Abbreviations:

SD: standard deviation

n, number of participants

*AD= Alzheimer’s Disease, Mixed = mixed form of dementia

† MMSE= Folstein Mini-mental State Examination (scores range from 0 to 30, higher scores representing better function),

‡ ADAS-Cog= Alzheimer’s Disease Assessment Scale (score ranges from 0 to 70 with higher scores suggesting greater impairment, 59 missing data),

Table 2. Gray Matter Volume Difference Between High versus Low Dual-Task Cost (n= 336 for CB DTC and n= 333 for NA DTC):

			GMV (mL)
Cluster No.	Cluster Size (no. of voxel)	Brain Region (cluster peak)	CB DTC <20%	CB DTC ≥20%	MNI Coordinates of the maximum intensity voxel			TFCE-value	*p-FWE-c*
			(n=294)	(n=42)
1	1723	Left Middle Temporal	1.2±0.2	1.1±0.2	-45	-2	-18	1123	0.01
2	3091	Right Temporal Pole	1.5±0.2	1.4±0.2	36	17	-33	1093	0.01
3	412	Left Middle Temporal	1.2±0.2	1.1±0.2	-66	-15	-14	891	0.03
4	425	Left Inferior Temporal	1.6±0.3	1.5±0.2	-42	-8	-39	890	0.03
5	101	Left Inferior Temporal	1.5±0.2	1.4±0.2	-54	-42	-21	807	0.049
			NA DTC <20%	NA DTC ≥20%
			(n=199)	(n=134)
1	35883	Left Lingual	1.2±0.2	1.1±0.2	-2	-72	-5	2951	<0.001
2	8550	Right Inferior Temporal	1.5±0.2	1.4±0.2	60	-21	-26	2291	<0.001
3	756	Left Cuneus	1.2±0.2	1.1±0.2	-9	-83	35	1909	<0.001
4	631	Left Angular	1.4±0.2	1.3±0.2	-45	-74	32	1784	<0.001
5	160	Right Superior Frontal	0.9±0.1	0.8±0.1	20	69	8	1753	<0.001
6	360	Left Precentral	1.4±0.2	1.3±0.2	-44	-2	45	1744	<0.001
7	13	Right Cerebellum	2.4±0.3	2.3±0.3	14	-63	-15	1721	<0.001
8	273	Right Superior Medial Frontal	1.3±0.2	1.2±0.2	11	56	24	1720	<0.001
9	46	Left Occipital Superior	0.9±0.2	0.8±0.2	-14	-99	12	1717	<0.001
10	58	Right Medioorbital Frontal	0.9±0.1	0.8±0.1	9	69	-8	1704	<0.001
11	18	Left Occipital Superior	1.4±0.2	1.3±0.2	-14	-71	39	1684	<0.001

NOTE. The anatomical locations were based on the Automated Anatomical Labeling version 3 Atlas (AAL3). TFCE p-value<0.05 family-wise error corrected, cluster-size threshold >10 voxels; x,y,z=the original SPM coordinates in millimeters of the MNI space; in case of multiple peaks in the same anatomic area of a cluster, only the maximal peak is reported.

Adjusted from age, sex, educational level, TIV, WMHs and MRI type.

CB=counting backward, NA=naming animals

Table 3. Interaction Between the Naming Animal Dual-task Cost (NA DTC) Level and the Cognitive Status on the Gray Matter Volume (GMV) with a conditional effect when applicable:

Predictor (GMv Cluster)	Moderator (Condition)	B	SE	p-value	95% CI
Left Precentral	MCI vs. Control groups (n=289)	0.01	0.05	0.85	[-0.09, 0.11]
	Dementia vs. Control groups (n=165)	-0.07	0.03	0.03	[-0.14, -0.01]
	Dementia group (n=44)	-0.18	0.06	0.002	[-0.29, 0.07]
	Control group (n=121)	-0.03	0.04	0.47	[-0.10, 0.05]
	Dementia vs. MCI (n=212)	-0.17	0.06	0.01	[-0.29, -0.04]
	Dementia group (n=44)	-0.18	0.06	0.002	[-0.29, -0.07]
	MCI group (n=168)	-0.01	0.03	0.68	[-0.07, 0.05]

NOTE. For clarity, only significant results are shown in the table; for exhaustive results, see supplementary material.

P-value significant (i.e,<0.05) in bold.

MCI, mild cognitive impairment

No competing interests reported.

supplmentarymaterial.docx

Mapping the neural substrate of dual-task gait cost in older adults across the cognitive spectrum

Status:

Version 1

Abstract

Figures

Introduction

Methods

Population:

Clinical assessment:

MRI acquisition:

MRI processing:

Statistics:

Results

Cohort characteristics:

Brain gray matter volume (GMV) difference following the load of dual task cost (DTC):

Moderation analysis:

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1