Original contributionWhite matter hyperintensities and dynamics of postural control☆
Introduction
White matter hyperintensities (WMHs) are seen as multifocal and/or diffuse hyperintense areas on T2-weighted brain MRIs of older people. WMHs have been related pathologically to cerebral microangiopathy [1], [2], hypoperfusion [3] and neuronal loss in affected areas [4], [5]. WMHs are strongly associated with age, hypertension and diabetes, thus supporting a vascular hypothesis of their origin. Clinical studies have suggested a link between WMHs and the age-related frontal lobe syndrome of cognitive decline [6], [7], balance disorders and falls [8]. Specifically, it appears that the fronto-temporal cortex [9] and periventricular white matter [10] are particularly vulnerable to hypoperfusion and the development of WMHs. Because of the close proximity of frontal-subcortical circuits that control both motor and cognitive functions, it would not be surprising that periventricular WMHs may simultaneously cause dysfunction in both systems and that neuroanatomical changes in these structures have consequences for memory, executive functions and balance in the older adults. We hypothesized that WMHs may affect feedback mechanisms controlling the dynamics of postural control. This study assessed the relationship between WMHs on MRI and postural control in community-living older people without history of falls.
Section snippets
Participants
Postural control assessment and MRI studies were conducted in the SAFE (Syncope and Falls in the Elderly) Laboratory and at the Magnetic Resonance Imaging Center at the Beth Israel Deaconess Medical Center (BIDMC) in a GE 3-T VHI scanner. We enrolled consecutively 104 participants, aged >50 years, who provided informed consent, approved by the Institutional Review Boards at the BIDMC and the Joslin Diabetes Center to participate in observational physiological studies about the effects of
Characteristics of study cohort
A total of 76 subjects completed MRI, gait and balance assessments (Table 1). Because of the known associations between WMHs, hypertension and diabetes, we also present separately the demographic characteristics for 38 normotensive (NTN) controls: 14 diabetic-normotensive (DM-NTN), 10 diabetic-hypertensive (DM-HTN) and 14 nondiabetic-hypertensive (HTN) participants. Whole brain and white matter volumes were not different among the groups. Participants with hypertension had greater normalized
Discussion
This study demonstrated that diffuse periventricular hyperintensities and ischemic punctuate lesions affect the dynamics of postural control in older adults without a history of falls. WMHs were associated with reduced range of postural sway in the anteroposterior direction and increased sway in the mediolateral direction and more random fluctuations in both directions, suggesting negative effects on postural control feedback mechanisms. Increased WMH load affected certain parameters of dynamic
Acknowledgments
We would like to acknowledge the contributions of the nurses from General Clinical Research Center; Sarah LaRose, BS, Laura DesRochers, BS, from the Division of Gerontology; Rob Marquise, BS, Fontini Kourtelidis, BS, and Susan LaRuche, BS, from the Radiology Department, Beth Israel Deaconess Medical Center, for their help with data acquisition.
References (41)
- et al.
Partial volume effects in MRI studies of multiple sclerosis
Magn Reson Imaging
(1999) - et al.
Aging, muscle activity, and balance control: physiologic changes associated with balance impairment
Gait Posture
(2003) - et al.
Ability of static and statistical mechanics posturographic measures to distinguish between age and fall risk
J Biomech
(2005) - et al.
The influence of foot position on standing balance
J Biomech
(1987) - et al.
Effect of loss of balance on biomechanics platform measures of sway: influence of stance and a method for adjustment
J Biomech
(1990) - et al.
Risk factors for cerebral hypoperfusion, mild cognitive impairment, and dementia
Neurobiol Aging
(2000) - et al.
Periventricular lesions in the white matter on magnetic resonance imaging in the elderly. A morphometric correlation with arteriolosclerosis and dilated perivascular spaces
Brain
(1991) - et al.
Pathologic findings of silent, small hyperintense foci in the basal ganglia and thalamus on MRI
Neurology
(1999) - et al.
Cerebral perfusion and cerebrovascular reactivity are reduced in white matter hyperintensities
Stroke
(2002) - et al.
Pathophysiologic mechanisms in the development of age-related white matter changes of the brain
Dement Geriatr Cogn Disord
(1998)
Pathogenesis of leukoaraoisis: a review
Stroke
The cognitive correlates of white matter abnormalities in normal aging: a quantitative review
Neuropsychology
Risk factors for cerebrovascular disease as correlate of cognitive function in a stroke-free cohort
Arch Neurol
Relationship between balance and abnormalities in cerebral magnetic resonance imaging in older adults
Arch Neurol
Regional cerebral hypoperfusion in long-term type 1 (insulin-dependent) diabetic patients: relation to hypoglycaemic event
Nucl Med Commun
Brain metabolic alterations in patients with type 1 diabetes-hyperglycemia-induced injury
J Cereb Blood Flow Metab
A new rating scale for age-related white matter changes applicable to MRI and CT
Stroke
Global and regional effects of type 2 diabetes mellitus on brain tissue volumes and cerebral vasoreactivity
Diabetes Care
Open-loop and closed-loop control of posture: a random-walk analysis of center-of-pressure trajectories
Exp Brain Res
Random walking during quiet standing
Phys Rev Lett
Cited by (42)
The effect of diabetic retinopathy on standing posture during optic flow stimulation
2022, Gait and PostureCitation Excerpt :Moreover, white matter hyperintensities (WMHs), a diffuse hyperintense areas, secondary to vascular complications, are strongly associated with age, hypertension and diabetes [17]. In older adults, Novak et al. [17], have found a correspondence between the WHMs of the fronto-temporal and parieto-occipital regions with the increased postural instability in both mediolateral and anteroposterior direction. The main function of the occipital area is to process visual information and visual perceptions.
Center of pressure plausibility for the double-link human stance model under the intermittent control paradigm
2021, Journal of BiomechanicsIs Physical Frailty a Neuromuscular Condition?
2019, Journal of the American Medical Directors AssociationThe neural correlates of discrete gait characteristics in ageing: A structured review
2019, Neuroscience and Biobehavioral ReviewsCitation Excerpt :Eight different quantitative gait measurement techniques were reported. Twenty-seven studies reported the use of gait walkway systems (Rosano et al., 2005a, b; Rosano et al., 2008; Nadkarni et al., 2009; Zimmerman et al., 2009; Murray et al., 2010; de Laat et al., 2011a, b, c; Choi et al., 2012; de Laat et al., 2012; Callisaya et al., 2013; Annweiler et al., 2014; Bolandzadeh et al., 2014; Callisaya et al., 2014; Nadkarni et al., 2014; Rosso et al., 2014; Beauchet et al., 2015; Ezzati et al., 2015; Holtzer et al., 2015; Yuan et al., 2015; Rosario et al., 2016; Verlinden et al., 2016; Beauchet et al., 2017; Wennberg et al., 2017a; Fling et al., 2018; van der Holst et al., 2018), one study used both a gait walkway and inertial sensors to measure different gait characteristics (Fling et al., 2016), two studies utilised two photoelectric cells connected to a chronometer (Soumaré et al., 2009; Dumurgier et al., 2012), footswitches were used in two studies (Manor et al., 2012; Beauchet et al., 2014), an accelerometer was described in one study (Stijntjes et al., 2016), one study made use of a treadmill (Shimada et al., 2013), one study described the use of reflective markers with a motion analysis system during treadmill walking (Bruijn et al., 2014) and 17 studies derived gait characteristics from timed walks (Rosano et al., 2005b; Wolfson et al., 2005; Della Nave et al., 2007; Rosano et al., 2007a; Baezner et al., 2008; Novak et al., 2009; Frederiksen et al., 2011; Ryberg et al., 2011; Sorond et al., 2011; Moscufo et al., 2012; Willey et al., 2013; Wolfson et al., 2013; Sakurai et al., 2014; del Campo et al. (2016); Nadkarni et al., 2017; Sakurai et al., 2017; Tian et al., 2017b). Gait characteristics relating to intraindividual variability were derived either as the standard deviation of the variability within the original measurement (Verlinden et al., 2016; Zimmerman et al., 2009) or as a coefficient of variance of the measurement (Annweiler et al., 2014; Beauchet et al., 2014, 2015, 2017; de Laat et al., 2011c; Manor et al., 2012; Rosano et al., 2007b; Rosso et al., 2014; Shimada et al., 2013; Wennberg et al., 2017a).
- ☆
This study was supported by American Diabetes Association Grants 1-03-CR-23 and 1-06-CR-25, and NIH-NINDS 1R01-NS045745-01A2 to V. Novak, an NIH Older American Independence Center Grant 2P60 AG08812 and a General Clinical Research Center (GCRC) Grant MO1-RR01032.