Evaluating Peripersonal Space through the Functional Transcranial Doppler: Are We Paving the Way for Early Detecting Mild Cognitive Impairment to Dementia Conversion?

Abstract

Background:

Identifying the patients with mild cognitive impairment (MCI) who may develop dementia (MDC) is challenging. The study of peripersonal space (PPS) by using functional transcranial Doppler (fTCD) could be used for this purpose.

Objective:

To identify changes in cerebral blood flow (CBF) during motor tasks targeting PPS, which can predict MDC.

Methods:

We evaluated the changes in CBF in 22 patients with MCI and 23 with dementia [Alzheimer’s disease (AD) and vascular dementia (VaD)] during a motor task (passive mobilization, motor imagery, and movement observation) in which the hand of the subject moved forward and backward the face.

Results:

CBF increased when the hand approached the face and decreased when the hand moved from the face in the healthy controls (HCs). CBF changed were detectable only in patients with MCI but not in those with the AD and those who were MDC after 8-month follow-up. On the other hand, the patients with VaD presented a paradoxical response to the motor task (i.e., a decrease of CBF rather than an increase, as observed in HCs and MCI). Therefore, we found a modulation of PPS-related CBF only in HCs and patients with stable MCI (at the 8-month follow-up).

Conclusions:

fTCD may allow preliminarily differentiating and following-up the patients with MCI and MDC, thus allowing the physician to plan beforehand more individualized cognitive rehabilitative training.

Keywords

Dementia functional transcranial Doppler mild cognitive impairment MCI-to-dementia conversion peripersonal space

INTRODUCTION

A slight, progressive decline in cognitive functions can be part of the natural aging of human beings. On the contrary, a gradual, greater cognitive decline in different domains (including memory, attention, language, visual-spatial abilities, judgment, and problem-solving skills) than expected due to aging and with an impairment in the ability to carry the activities of daily life (ADL), characterizes the broad category of dementias [1]. Other common symptoms include psychological and psychiatric changes, whereas consciousness is usually not affected. The wide spectrum of dementias is the result of the destruction of brain neuronal circuits, whose extent and the anatomopathological frameworks account for different types of dementia with diverse onset and symptomatology, including Alzheimer’s disease (AD) and vascular dementia (VaD) [2 –4].

The insidious and progressive decline in memory functions without any impact on ADL is defined mild cognitive impairment (MCI). The differential diagnosis between MCI and early dementia is not always straightforward from a clinical point of view [5]. Moreover, MCI is considered as a pre-dementia stage given that the rate of MCI-to-dementia conversion (MDC) is not negligible (∼12–14%) [5]. However, identifying the patients among those with MCI who are at risk of MDC is challenging, being aging the only known major risk factor for both MCI and dementia, beyond arterial hypertension, smoking, diabetes, and obesity. Neuropsychological, advanced functional neuroimaging and neurophysiological investigations, and cerebrospinal fluid tests have been proposed as objective tools for MDC diagnosis [6, 7], but the amount of evidence supporting their validity is of variable strength [6, 7]. Therefore, there is a need for other, more objective approaches that can help in identifying the patients at risk of MDC.

Cerebral hypoperfusion is deemed as either an aggravating factor for cognitive impairment or a constitutive component of the pathophysiology of MCI or dementia [8 –14]. Therefore, functional transcranial Doppler (fTCD) could be usefully used to assess the vascular status of brain circulation in patients with MCI and early dementia [15, 16]. However, less information is available concerning MDC [15, 16].

Given that the assessment of physiologic measures, including cerebral perfusion, during a cognitive task may increase the ability to detect subtle functional changes in the early disease stages [10], it could be useful to investigate such brain perfusion variability in patients with MCI in order to identify those individuals at risk of MDC [10 –14]. Suitable tasks for this kind of patients may be represented by those entraining the peripersonal space (PPS). In fact, the abnormal functioning of the neural network supporting PPS functions may account for some behavioral disorders in patients with cognitive decline, including behavioral excesses (e.g., disruptive vocalization, wandering, and aggression) and deficits (dependency in social interaction and communication), as also shown in different neuropsychiatric conditions [17, 18], even though PPS naturally deteriorates in normal elderly [19]. Nonetheless, all these concerns can be related to MDC [20 –23].

PPS functions depend on vast, multisensory neural networks (encompassing dorsal and ventral premotor areas and the anterior portion of the intraparietal sulcus) integrating the neural representation of the body (the “body schema”) and of the space around the body (PPS). This allows to plan avoiding and manipulating behaviors and to guide the movements of the body through the space. This PPS-based motor control occurs by constantly monitoring the position and the movements of body parts about nearby objects [24].

Functional neuroimaging has been used to investigate the cerebral blood flow (CBF) variations during tasks targeting the PPS, in order to identify the anatomical underpinnings and the functional mechanisms of PPS in healthy humans [25]. Likewise, CBF recording by fTCD has proven to be an effective, safe, repeatable, and painless tool to measure the CBF variations correlated to cognitive functions [17 , 27]. Patients with dementia and MCI may have a different degree of impairment in brain activations during the delivery of cognitive tasks, probably in reason of a cortical-subcortical connectivity breakdown following a progressive neurovascular degeneration, which simultaneously corresponds to a different degree of cognitive impairment) [28]. Therefore, we hypothesized that patients with dementia and those with MCI who could be at risk of MDC may not show a modulation of CBF during cognitive tasks, unlike normal elderly and individuals with MCI who could not be at risk of MDC. Our study aimed to find a possible correlation between the CBF modulations recorded during a motor-cognitive task perturbing PPS by using fTCD, in order to identify the patients with MCI at risk of MDC whether showing clear CBF abnormalities during cognitive tasks. This issue may be important to achieve a more careful and patient-tailored management.

Materials and methods

Subjects

We enrolled 45 patients, aged >55 years, able to read and write, who attended the Neurocognitive Laboratory of our Institute (Table 1). Individuals with a symptomatic hemodynamic stenosis of the neck vessels, a severe cognitive decline due to other medical, psychiatric, or neurologic conditions than AD, VaD, and MCI, severe limitation of upper limb movements, and intake of vasoactive agents were excluded from the study. We also enrolled 20 healthy controls (HC) chosen among the caregivers and researchers’ acquaintances. The research followed the principles of the Helsinki Declaration and was approved by the Ethics Committee of the Institute. All the participants gave their written informed consent to study participation.

Table 1

Clinical-demographic characteristics

Group	MMSE	CRD-M	Gender (M:F)	Age	Education	dd	risk factors (% of patients)
							None	Smoke	Diabetes mellitus	Hypertension	Coagulopathies	Hyperlipidemia	History of stroke
AD (11)	8±2	16±4	6:5	70±13	8±2	6±2	53	47	53	7	5	33	0
MCI (15)	22±6	6±2	5:10	71±15	9±2	6±2	73	27	27	27	0	33	40
MDC (7)	21±5	6±2	4:3	70±12	8±2	7±2	50	14	0	14	0	28	15
VaD (12)	10±2	16±4	7:5	70±18	10±2	6±2	0	20	7	40	20	40	80
HC (20)	28±1		8:12	72±9	9±2		70	30	0	10	0	0	0

MMSE, Mini-Mental State Examination; CDR-M, Clinical Dementia Rating-Modified; AD, Alzheimer’s disease; VaD, vascular dementia; MCI, mild cognitive impairment; MDC, MCI-to-dementia conversion; dd, disease duration.

Patients were diagnosed with MCI, VaD, or AD according to the available diagnostic guidelines [29 –31]. Patients with MCI had an abnormal performance in one or more tests from the neuropsychological battery (1–1.5 SD below the expected performance for age and education), a CDR stage 0.5. Patients with AD had a documented deterioration in memory and other cognitive functions with a reduced ability in performing ADL, and a CDR stage >1. Patients with VaD had an impairment of memory and at least two other cognitive domains, interfering with ADL execution; a cerebrovascular disease defined by the presence of focal signs consistent with stroke, and evidence of non-relevant cerebrovascular disease; a causal and temporal relationship between the two abovementioned disorders.

Experimental procedure

All the patients underwent a neuropsychological tests battery, including the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating-Modified (CDR-M). Then, each participant underwent fTCD examination. The participant was lying on a bed in a silent, mild-lighted room, with open eyes. The right limb was located along the trunk with the palm facing up, so that the right arm was in a position that allowed the forearm to move toward the face (without touching it) (Fig. 1). The head was held in position by a headrest to minimize head movements. Moreover, the participant wore a firm and adjustable headband to hold the transducers securely in the optimal acoustic window. All the participants were forbidden to take caffeine, alcohol, and nicotine up to 24 h before fTCD assessment, due to their known influence on vascular reactivity. Using fTCD, we measured the pulsatility index (PI) from the middle cerebral artery during resting condition (rest hand-position) and two different motor tasks. The resting condition allowed establishing the regulatory parameters for the subsequent sessions. We focused on this branch, as previous reports outlined no significant ipsilateral and contralateral differences among the main brain vessels during visuomotor cognitive tasks [32 –36].

Fig.1

Experimental paradigm. A) Passive movement condition; B) movement imagery condition; C) movement observation condition.

In addition, blood pressure (from left upper limb) and heart rate were monitored continuously. In the “passive condition”, an examiner mobilized the right upper limb in two positions. First, the examiner flexed the forearm on the arm up to 10° toward the face (i.e., forward hand-position). The passive movements lasted 15 heartbeats, and the velocity of was kept as constant as possible. The hand was kept in front of the face for further 5 heartbeats. Then, the examiner extended the forearm at 180° again, at the same velocity and number of heartbeats (i.e., backward hand-position) (Fig. 1) [37]. This experiment was performed to verify the role that the two afferent pathways (optical and proprioceptive) can play in modulating the cerebral hemodynamic responses. In the “motor imagery condition”, the same movements occurred imaginary in the same movement positions and rate (that was set by a heartbeat-based acoustic cue). This experiment was conducted to assess whether and how the motor images affect brain hemodynamics. As a control experiment, we evaluated the effects of the hand of the experimenter going forward and backward the face of the participant in a “passive condition” fashion (namely, “movement observation”).

The fTCD was performed using a conventional ultrasonic encoded color system equipped with a 2–5 MHz phase transducer (iU22 Philips Healthcare Solutions, Bothell, WA). The examination was performed through the left acoustic bony windows and the transducer placed in front of the tragus and upward the zygomatic arc. The peak systolic velocity, the end-diastolic velocity, the Mean velocity were measured and averaged from each main intracranial vessel. PI was calculated according to the formula to estimate the microvascular resistance distal to the measurement site [9] and its influence on resting-state CBF [38]. Each fTCD recording was performed by two different fTCD raters and twice by each or them, to estimate the inter-trial variability (ITV) of the results in each HC, the inter-subject reliability (ISR), and the inter-rater reliability (IRR). The values with the lowest SD were chosen for further analyses. The experimenter who performed the fTCD was blind on patient’s diagnosis.

Statistical analysis

PI was analyzed by using an ANOVA with the factors hand-position (three levels: rest, forward, and backward), condition (three levels: passive movement, motor imagery, and movement observation), and group (three levels: HC, MCI, and dementia). The Greenhouse-Geisser method was used, if necessary, to correct for non-sphericity. Conditional on a significant F-value, post-hoc t-tests were performed to explore the strength of main effects and the patterns of interaction between experimental factors (Bonferroni correction). A p-value of <0.05 was considered significant. Clinical-demographic characteristics were included as covariates in the multivariate analysis. β-value (standardized regression coefficients), which is a measure of how strongly each predictor variable influences the criterion (dependent) variable, are provided. The higher the β-value, the greater the impact of the predictor variable on the criterion variable. If the β-value is equal or nearly to zero, then there is no relationship between the variables. All data are given as mean±SD. Finally, the sensitivity and specificity of the PI in the different conditions to distinguish accurately between MCI and MDC after a follow-up period of 8 months were calculated by measuring the Area under the Receiver Operating Characteristic Curve (AUC). Before proposing our approach as a clinically valid, we estimated the ITV of the results in HC according to the following formula: $\frac{100 \times (V_{2} - V_{1})}{0.5 \times (V_{1} + V_{2})}$ , where V₁ and V₂ are the subsequent determinations [39]. To assess ISR, the ITV values of PI were used to calculate the Intraclass Correlation Coefficient (ICC) [40]. Values greater than 0.7 indicates the measurement as reliable. Last, the IRR was calculated as percent agreement between raters [41].

RESULTS

All the subjects completed the experimental procedure without any adverse event. PI of the patients at rest was above the HC values of about 20–30%, without significant MCI-dementia differences (Fig. 2).

The PI modulation was significantly different among groups, hand position, and condition (hand-position×condition×group F = 6.2, p < 0.001). The factor hand position significantly influenced the direction and magnitude of PI changes in each group and condition. Figures 2–3 illustrate mean PI changes and, respectively, examples of insonation depth-wise changes in CBF in HC, patients with dementia (AD and VaD), and patients with MCI (including the MDC). The data of ITV, ISR, and IRR for HCs are summarized in Table 2.

Fig.2

Summary of mean pulsatility index (PI) values across the two-movement direction (forward-backward) for each motor task (MI, motor imagery; PM, passive mobilization; MO, movement observation) in each group of subjects (AD, Alzheimer’s disease; VaD, vascular dementia; MCI, mild cognitive impairment; MDC, MCI-to-dementia conversion). Vertical error bars indicate SD. Asterisks refer to the significance of the PI values across the two-movement direction (forward and backward) as compared to rest (***p < 0.001, **0.001 < p < 0.01, *0.01 < p < 0.05).

Fig.3

Cerebral blood flow (CBF) from the middle cerebral artery at rest, during motor imagery (MI), passive mobilization (PM), and movement observation (MO) in representative subjects from each group (AD, Alzheimer’s disease; VaD, vascular dementia; MCI, mild cognitive impairment; MDC, MCI-to-dementia conversion; HC, healthy controls) comparable for depths of middle cerebral artery insonation.

Table 2

Summary of inter-trial variability (ITV), the inter-subject reliability (ISR), and the inter-rater reliability (IRR) (as Intraclass Correlation Coefficient, ICC) of the pulsatility index results in each group (AD, Alzheimer’s disease; VaD, vascular dementia; MCI, mild cognitive impairment; MDC, MCI-to-dementia conversion) during motor imagery (MI), passive movement (PM), and movement observation (MO)

	ITV (%)			ISR (ICC)			IRR
	rest	forward	backward	rest	forward	backward	rest 87%	forward 88%	backward 98%
AD
MI	7	1	–6	0.88	0.84	0.79	1	1	1
PM	4	4	–6	0.80	0.82	0.85	0	1	1
MO	3	–8	3	0.89	0.88	0.91	1	1	1
MCI
MI	2	1	–1	0.91	0.82	0.89	1	1	1
PM	–2	6	0	0.84	0.93	0.79	1	1	1
MO	–8	8	–9	0.79	0.78	0.78	1	1	1
MDC
MI	0	8	–3	0.93	0.87	0.80	1	0	1
PM	6	2	1	0.88	0.91	0.86	1	1	1
MO	–2	–3	5	0.84	0.92	0.89	0	0	1
VaD
MI	7	–8	8	0.94	0.78	0.79	1	1	1
PM	4	4	–7	0.91	0.88	0.84	1	1	1
MO	4	–9	–6	0.92	0.84	0.94	1	1	1
HC
MI	–1	–10	4	0.81	0.84	0.81	1	1	1
PM	–7	1	–3	0.92	0.90	0.83	1	1	1
MO	0	5	–6	0.79	0.95	0.86	1	1	1

Specifically, PI was significantly modulated during motor imagery (group×hand-position F = 14, p < 0.001), with a significant hand-position effect in HC (F = 36, p < 0.001), whereas patients with MCI and dementia showed no PI modulation (both p = 0.4) (HC-MCI and HC-dementia comparison, p < 0.001; MCI-dementia comparison p = 0.9). In detail, PI increased when the hand approached the face and decreased in the opposite direction in all HCs (waxing and waning profile) (Fig. 2). Patients with MCI showed two different profile: one group (7 individuals, namely those being labeled MDC) showed no PI changes, as the patients with dementia (both all VaD and all AD) (Fig. 2); the other group (i.e., the remaining 15 patients with MCI) had a reverse profile of PI (i.e., waning and waxing) (Fig. 2).

Similarly, ANOVA showed a significant group×hand-position interaction during passive movement (F = 5.7, p < 0.001), with a significant hand-position effect in each group (HC: F = 34, p < 0.001; MCI: F = 2.7, p = 0.04; dementia: F = 4.9, p = 0.007). Specifically, all HCs and 15 patients with MCI showed a waxing and waning profile, whereas the other seven patients with MCI (namely those labeled as MDC) and all the patients with VaD had a waning and waxing profile (Fig. 2). The remaining patients with dementia (i.e., all those with AD) showed no PI changes (HC-MCI comparison p = 0.1; HC-dementia comparison p < 0.001).

Concerning movement observation, we found a significant hand-position effect (F = 102, p < 0.001) (Fig. 2), with a waning and waxing of PI that was nearly identical among all the participants (each comparison p = 0.7).

To summarize, all HCs always showed a waxing and waning of PI. Seven patients with MCI (namely those labeled as MDC) showed no PI changes during motor imagery and a waning and waxing of PI during passive movement. The remaining patients with MCI showed a waning and waxing of PI during motor imagery, and a waxing and waning of PI during passive movement. All the patients with VaD showed no PI changes during motor imagery and a waning and waxing of PI during passive movement. All other patients with dementia, namely those with the AD, showed no PI changes. All the included participants showed a waxing and waning of PI in the movement observation.

The clinical-demographic characteristics (MMSE β= 0.01; crd-m β= 0.02; gender β= 0.01; age β= 0.05; education β= 0.04; risk factors β= 0.05; and disease duration β= 0.01; all p > 0.1) did not influence the changes of the dependent variable PI across motor tasks. ROC analysis showed that the PI modulation during motor imagery condition preceded the diagnostic categorization of MCI at 8-month follow-up, i.e., MCI and MDC, with an AUC of 0.6 (Fig. 4). Indeed, the patients with a higher PI modulation remained clinically stable, whereas the patients with a demodulated PI converted into dementia.

Fig.4

The AUC of the ROC illustrates the power of pulsatility index modulation during motor imagery in predicting the MCI-to-dementia conversion at 8-month follow-up.

The measures showed a significantly low trial-by-trial variability (from –9 to 9%, p = 0.2), a significantly high ISR (ICC≥0.8, p < 0.001), and a significantly high IRR (more than 86%, p < 0.001) (Table 2).

DISCUSSION

It has been shown that CBF evaluation could be a sensitive indicator of hypoperfusion in the early stages of dementia/MCI, when neuronal activity is already reduced, but neuropathology has not largely spread [42]. In fact, cross-sectional and longitudinal TCD studies showed a correlation between vascular damage and cognitive decline, given that greater increase of PI (i.e., a higher cerebrovascular resistance) was found in resting condition in more severe cognitive decline [15 , 42–44]. Therefore, CBF evaluation may be an effective tool in the characterization and tracking of MCI and dementias.

To the best of our knowledge, this is the first study investigating PPS functions by using fTCD, i.e., TCD during functional tasks, in patients with cognitive decline. First, the data concerning ITV, ISR, and IRR indicate that fTCD seems to be a clinically valid approach, although larger samples are required to confirm this affirmation. In keeping with this issue, we can claim that our study sharpens the understanding of the association between PI and cognitive decline [43 –45]. In fact, our approach highlighted a different PI modulation in patients with MCI; fifteen among them showed CBF variations related to PPS-face violation similar to those observed in HC, whereas the others showed a paradoxical PI modulation, similarly to some individuals with dementia. Of note, such CBF modulation did not correlate with the neuropsychological profile. Given the small sample enrolled, we have to be cautious in affirming that the paradoxical PI modulation shared by some among the patients with MCI and with dementia would necessarily indicate the patients with MCI who are at risk of MDC. Notwithstanding, we believe that these patients should undergo a more strict follow-up. In fact, an increase in PI reflects a reduced CBF and, thus, a failure of the neurovascular coupling (NVC) (i.e., the CBF increase due to neuronal activation) [46] to perform an effortful cognitive-motor task. The detection of NVC failure in patients with cognitive decline is of notable importance, given that it significantly contributes to neurodegeneration and, consequently, cognitive impairment in the AD and VaD [47] and may be a predictor of rapid MDC [48]. In fact, chronic hypoperfusion may lead to neuronal degeneration and, then, hippocampal atrophy, the progression of white matter damage, cognitive decline, and dementia [47].

Even though VaD and AD share similar risk factors, it is not clear whether vascular and degenerative damages coexist or the vascular damage triggers or accelerates the degenerative one [49, 50]. However, the detection of vascular impairment in patients with MCI is important to design patient-tailored management [48]. In fact, the NVC failure further induces neuronal degeneration and also produces a connectivity breakdown among the parietal-temporal and frontal regions that are the primary structures affected by neurodegeneration, and account for the reduced performance at neuropsychological assessment [51].

The NVC failure may depend on the dissociation of the neurovascular units, i.e., the complex formed by endothelial cells, neurons, and neuroglia [52], secondary to neurodegeneration and/or vascular damage. Neurodegeneration induces per se a microvascular damage (that, in turn, makes the brain more susceptible to ischemic damage and, thus, neurodegeneration) [53], which both account for CBF alterations [54]. In fact, amyloid-β (Aβ) may increase vascular smooth muscle tone, induce a chronic cerebral hypoperfusion that interferes the clearance of Aβ, and overproduce Aβ [38]. CBF reduction could be compensated in some patients with MCI using a regional hyperperfusion, mainly within temporal lobes, which may explain the paradoxical PI decrease we found [55, 56]. Therefore, we may argue that the patient with MCI who do not show such compensatory mechanisms (i.e., the paradoxical PI decrease) may be at risk of MDC. This compensation mainly depends on astrocytes activation, which not only regulates capillary diameters [55] but also stimulates the mitogenic activity of capillary endothelial cells, suggesting that prolonged functional hyperemia may initiate angiogenesis that could result in increased capillary density [57, 58]. Moreover, inflammatory processes related to neurodegeneration locally induce chronic inflammatory response [59, 60], which in turn increases angiogenesis [61 –63].

It is noteworthy that the patients with VaD also showed a paradoxical PI modulation as compared to the patients with the AD. Therefore, we may argue that the vascular damage in the AD may be distinct from that observed in VaD, being probably a trait of the disease. Anyway, vascular damage aggravates the dementia course and fosters MDC, with particular regard to amnestic MCI. This further confirms the importance of fTCD as a screening tool for assessing the vascular status of brain circulation in the preclinical and clinical stages of dementias and MCI.

One could concern that we did not assess fTCD differences between the three main brain vessels. However, no relevant between-vessel differences have been reported in HC [64, 65] as well as in patients with cognitive decline [32–36 , 66]. Moreover, it is necessary to acknowledge the possible risk that the individuals with dementia did not perform the motor imagery task correctly, being unable to understand or carrying the task, which may have influenced our results. Indeed, the patients with dementia had a CDR-M score of ∼16, which is the boundary between moderate and severe dementia, and were able to carry simple orders and tasks. Therefore, we may hypothesize that the lack of PI modulation during motor imagery task depended on a dysfunctional task execution rather than on the inability to understand the instructions. However, this issue needs to be further investigated in larger samples and using other motor tasks (e.g., active and passive-imagery task).

In conclusion, our study suggests the usefulness of sharpening CBF assessment using fTCD in patients with MCI and dementia by using cognitive task targeting PPS, beyond TCD in resting condition, so to help clinicians in differentiating these entities. While fTCD cannot match the spatial resolution of functional MRI and nuclear medicine investigation, it offers a superior temporal resolution and is easier to apply and more robust against movement artifacts. Once confirmed by larger cohort studies, it could be possible to propose the routine use of such an easy and quick ambulatory device to monitor patient’s status and adapt treatment, so to realize patient-tailored management.

DISCLOSURE STATEMENT

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/17-0973r1).

References

Burns

, Iliffe

(2009) Dementia. BMJ 338, b75.

Nelson

, Alafuzoff

, Bigio

, Bouras

, Braak

, Cairns

, Castellani

, Crain

, Davies

, Del Tredici

, Duyckaerts

, Frosch

, Haroutunian

, Hof

, Hulette

, Hyman

, Iwatsubo

, Jellinger

, Jicha

, Kövari

, Kukull

, Leverenz

, Love

, Mackenzie

, Mann

, Masliah

, McKee

, Montine

, Morris

, Schneider

, Sonnen

, Thal

, Trojanowski

, Troncoso

, Wisniewski

, Woltjer

, Beach

(2012) Correlation of Alzheimer’s disease, neuropathological changes with cognitive status, a review of the literature. J Neuropathol Exp Neurol 71, 362–381.

Scheltens

, Blennow

, Breteler

, de Strooper

, Frisoni

, Salloway

, Van der Flier

(2019) Alzheimer’s disease. Lancet 388, 505–517.

Tarawneh

, Holtzman

(2012) The clinical problem of symptomatic Alzheimer disease and mild cognitive impairment. Cold Spring Harb Perspect Biol 2, a006148.

Ward

, Tardiff

, Dye

, Arrighi

(2013) Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia, a systematic review of the literature. Dement Geriatr Cogn Disord Extra 3, 320–332.

Ahmed

, Paterson

, Warren

, Zetterberg

, O’Brien

, Fox

, Halliday

, Schott

(2014) Biomarkers in dementia, clinical utility and new directions. J Neurol Neurosurg Psychiatry 85, 1426–1434.

Drago

, Babiloni

, Bartrés-Faz

, Caroli

, Bosch

, Hensch

, Didic

, Klafki

, Pievani

, Jovicich

, Venturi

, Spitzer

, Vecchio

, Schoenknecht

, Wiltfang

, Redolfi

, Forloni

, Blin

, Irving

, Davis

, Hårdemark

, Frisoni

(2011) Disease tracking markers for Alzheimer’s disease at the prodromal (MCI) stage. J Alzheimers Dis 26, 159–199.

Roher

, Garami

, Tyas

, Maarouf

, Kokjohn

, Belohlavek

, Vedders

, Connor

, Sabbagh

, Beach

, Emmerling

(2011) Transcranial Doppler ultrasound blood flow velocity and pulsatility index as systemic indicators for Alzheimer’s disease. Alzheimers Dement 7, 445–455.

Tomek

, Urbanová

, Hort

(2014) Utility of transcranial ultrasound in predicting Alzheimer’s disease risk. J Alzheimers Dis 42, S365–S374.

10.

Xie

, Dolui

, Das

, Stockbower

, Daffner

, Rao

, Yushkevich

, Detre

, Wolk

(2016) A brain stress test, cerebral perfusion during memory encoding in mild cognitive impairment. Neuroimage Clin 11, 388–397.

11.

Jungwirth

, Zehetmayer

, Hinterberger

, Tragl

, Fischer

(2012) The validity of amnestic MCI and non-amnestic MCI at age 75 in the prediction of Alzheimer’s dementia and vascular dementia. Int Psychogeriatr 24, 959–966.

12.

Solfrizzi

, Panza

, Colacicco

, D’Introno

, Capurso

, Torres

, Grigoletto

, Maggi

, Del Parigi

, Reiman

(2004) Vascular risk factors, incidence of MCI, and rates of progression to dementia. Neurology 63, 1882–1891.

13.

, Caracciolo

, Wang

, Winblad

, Bäckman

, Qiu

, Fratiglioni

(2010) Accelerated progression from mild cognitive impairment to dementia in people with diabetes. Diabetes 59, 2928–2935.

14.

Wolters

, Zonneveld

, Hofman

, van der Lugt

, Koudstaal

, Vernooij

, Ikram

, Heart-Brain Connection Collaborative Research Group (2017) Cerebral perfusion and the risk of dementia, a population-based study. Circulation 136, 719–728.

15.

Malojcic

, Giannakopoulos

, Sorond

, Azevedo

, Diomedi

, Oblak

, Carraro

, Boban

, Olah

, Schreiber

, Pavlovic

, Garami

, Bornstein

, Rosengarten

(2017) Ultrasound and dynamic functional imaging in vascular cognitive impairment and Alzheimer’s disease. BMC Med 15, 27.

16.

Urbanova

, Tomek

, Mikulik

, Magerova

, Horinek

, Hort

(2014) Neurosonological examination, a non-invasive approach for the detection of cerebrovascular impairment in AD. Front Behav Neurosci 8, 4.

17.

Burgio

(1996) Direct observation of behavioral disturbances of dementia and their environmental context. Int Psychogeriatr 8, 343–349.

18.

Rapp

, Gutzmann

(2000) Invasions of personal space in demented and nondemented elderly. Int Psychogeriatr 12, 345–352.

19.

Costello

, Bloesch

, Davoli

, Panting

, Abrams

, Brockmole

(2015) Spatial representations in older adults are not modified by action, evidence from tool use. Psychol Aging 30, 656–668.

20.

Jacus

(2017) Awareness, apathy, and depression in Alzheimer’s disease and mild cognitive impairment. Brain Behav 7, e00661.

21.

Dillon

, Serrano

, Castro

, Leguizamón

, Heisecke

, Taragano

(2013) Behavioral symptoms related to cognitive impairment. Neuropsychiatr Dis Treat 9, 1443–1455.

22.

Chapman

, Mapstone

, McCrary

, Gardner

, Porsteinsson

, Sandoval

, Guillily

, Degrush

, Reilly

(2011) Predicting conversion from mild cognitive impairment to Alzheimer’s disease using neuropsychological tests and multivariate methods. J Clin Exp Neuropsychol 33, 187–199.

23.

Brooks

, Loewenstein

(2010) Assessing the progression of mild cognitive impairment to Alzheimer’s disease, current trends and future directions. Alzheimers Res Ther 2010 2, 28.

24.

Rizzolatti

, Fadiga

, Fogassi

, Gallese

(1997) The space around us. Science 277, 190–191.

25.

Grivaz

, Blanke

, Serino

(2016) Common and distinct brain regions processing multisensory bodily signals for peripersonal space and body ownership. Neuroimage 147, 602–618.

26.

Bleton

, Sejdić

(2015) A cerebral blood flow evaluation during cognitive tasks following a cervical spinal cord injury, a case study using transcranial Doppler recordings. Cogn Neurodyn 9, 615–626.

27.

Gatouillat

, Bleton

, VanSwearingen

, Perera

, Thompson

, Smith

, Sejdić

(2015) Cognitive tasks during walking affect cerebral blood flow signal features in middle cerebral arteries and their correlation to gait characteristics. Behav Brain Funct 11, 29.

28.

Farràs-Permanyer

, Guàrdia-Olmos

, Peró-Cebollero

(2015) Mild cognitive impairment and fMRI studies of brain functional connectivity, the state of the art. Front Psychol 6, 1095.

29.

McKhann

, Drachman

, Folstein

, Katzman

, Price

, Stadlan

(1984) Clinical diagnosis of Alzheimer’s disease, report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944.

30.

Roman

, Tatemichi

, Erkinjuntti

, Cummings

, Masdeu

, Garcia

, Amaducci

, Orgogozo

, Brun

, Hofman

, Moody

, O’Brien

, Yamaguchi

, Grafman

, Drayer

, Bennett

, Fisher

, Ogata

, Kokmen

, Bermejo

, Wolf

, Gorelick

, Bick

, Pajeau

, Bell

, DeCarli

, Culebras

, Korczyn

, Bogousslavsky

, Hartmann

, Scheinberg

(1993) Vascular dementia, diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 43, 250–260.

31.

Winblad

, Palmer

, Kivipelto

, Jelic

, Fratiglioni

, Wahlund

, Nordberg

, Bäckman

, Albert

, Almkvist

, Arai

, Basun

, Blennow

, de Leon

, DeCarli

, Erkinjuntti

, Giacobini

, Graff

, Hardy

, Jack

, Jorm

, Ritchie

, van Duijn

, Visser

, Petersen

(2004) Mild cognitive impairment—beyond controversies, towards a consensus, report of the International Working Group on Mild Cognitive Impairment. J Intern Med 256, 240–246.

32.

Sorond

, Schnyer

, Serrador

, Milberg

, Lipsitz

(2008) Cerebral blood flow regulation during cognitive tasks, Effects of healthy aging. Cortex 44, 179–184.

33.

Bleton

, Perera

, Sejdić

(2016) Cognitive tasks and cerebral blood flow through anterior cerebral arteries, a study via functional transcranial Doppler ultrasound recordings. BMC Med Imaging 16, 22.

34.

Vingerhoets

, Stroobant

(1999) Lateralization of cerebral blood flow velocity changes during cognitive tasks. A simultaneous bilateral transcranial Doppler study. Stroke 30, 2152–2158.

35.

, Huang

, Boninger

, Sejdić

(2014) An analysis of cerebral blood flow from middle cerebral arteries during cognitive tasks via functional transcranial Doppler recordings. Neurosci Res 84, 19–26.

36.

Kawashima

, Matsumura

, Sadato

, Naito

, Waki

, Nakamura

, Matsunami

, Fukuda

, Yonekura

(1998) Regional cerebral blood flow changes in human brain related to ipsilateral and contralateral complex hand movements–a PET study. Eur J Neurosci 10, 2254–2260.

37.

Bisio

, Garbarini

, Biggio

, Fossataro

, Ruggeri

, Bove

(2017) Dynamic shaping of the defensive peripersonal space through predictive motor mechanisms, when the “near” becomes “far”. J Neurosci 37, 2415–2424.

38.

Iadecola

(2010) The overlap between neurodegenerative and vascular factors in the pathogenesis of dementia. Acta Neuropathol 120, 287–296.

39.

Winer

(1972), Statistical principles in experimental design. McGraw-Hill, New York.

40.

Fleiss

(1986) The design and analysis of clinical experiments, New York, John Wiley.

41.

Landis

, Koch

(1977) The measurement of observer agreement for categorical data. Biometrics 33, 159–174.

42.

Lacalle-Aurioles

, Mateos-Pérez

, Guzmán-De-Villoria

, Olazarán

, Cruz-Orduña

, Alemán-Gómez

, Martino

, Desco

(2014) Cerebral blood flow is an earlier indicator of perfusion abnormalities than cerebral blood volume in Alzheimer’s disease. J Cereb Blood Flow Metab 34, 654–659.

43.

Altmann

, Thommessen

, Ronning

, Benth

, Reichenbach

, Fure

(2016) Middle cerebral artery pulsatility index is associated with cognitive impairment in lacunar stroke. J Neuroimaging 26, 431–435.

44.

Vicenzini

, Ricciardi

, Altieri

, Puccinelli

, Bonaffini

, Di Piero

, Lenzi

(2017) Cerebrovascular reactivity in degenerative and vascular dementia, A transcranial Doppler study. Eur Neurol 58, 84–89.

45.

Ruitenberg

, den Heijer

, Bakker

SLM

, van Swieten

, Koudstaal

, Hofman

, Breteler

MMB

(2005) Cerebral hypoperfusion and clinical onset of dementia, the Rotterdam study. Ann Neurol 57, 789–794.

46.

Lin

, Fox

, Hardies

, Duong

, Gao

(2010) Nonlinear coupling between cerebral blood flow, oxygen consumption, and ATP production in human visual cortex. Proc Natl Acad Sci U S A 107, 8446–8451.

47.

Luckhaus

, Flüb

, Wittsack

, Grass-Kapanke

, Jänner

, Khalili-Amiri

, Friedrich

, Supprian

, Gaebel

, Mödder

, Cohnen

(2008) Detection of changed regional cerebral blood flow in mild cognitive impairment and early Alzheimer’s dementia by perfusion-weighted magnetic resonance imaging. Neuroimage 40, 495–503.

48.

Zhao

, Gong

(2015) From chronic cerebral hypoperfusion to Alzheimer-like brain pathology and neurodegeneration. Cell Mol Neurobiol 35, 101–110.

49.

Iadecola

(2013) The pathobiology of vascular dementia. Neuron 80, 844–866.

50.

Zlokovic

(2011) Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat Rev Neurosci 12, 723–738.

51.

Jobst

, Smith

, Barker

, Wear

, King

, Smith

, Anslow

, Molyneux

, Shepstone

, Soper

, Holmes

, Robinson

, Hope

, Oppenheimer

, Brockbank

, McDonald

(1992) Association of atrophy of the medial temporal lobe with reduced blood flow in the posterior parietotemporal cortex in patients with a clinical and pathological diagnosis of Alzheimer’s disease. J Neurol, Neurosurg Psychiatry 55, 190–194.

52.

Muoio

, Persson

, Sendeski

(2014) The neurovascular unit - concept review. Acta Physiol (Oxf) 210, 790–798.

53.

Kokjohn

, Maarouf

, Roher

(2012) Is Alzheimer’s disease amyloidosis the result of a repair mechanism gone astray? Alzheimers Dement 8, 574–583.

54.

Kitaguchi

, Ihara

, Saiki

, Takahashi

, Tomimoto

(2007) Capillary beds are decreased in Alzheimer’s disease, but not in Binswanger’s disease. Neurosci Lett 417, 128–131.

55.

Dai

, Lopez

, Carmichael

, Becker

, Kuller

, Gach

(2009) Mild cognitive impairment and Alzheimer disease, patterns of altered cerebral blood flow at MR imaging. Radiology 250, 856–866.

56.

Dickerson

, Sperling

(2008) Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer’s disease, insights from functional MRI studies. Neuropsychologia 46, 1624–1635.

57.

Bell

, Ball

(1981) Morphometric comparison of hippocampal microvasculature in ageing and demented people, diameters and densities. Acta Neuropathol 53, 299–318.

58.

Harder

, Zhang

, Gebremedhin

(2002) Astrocytes function in matching blood flow to metabolic activity. News Physiol Sci 17, 27–31.

59.

Salminen

, Ojala

, Kauppinen

, Kaarniranta

, Suuronen

(2009) Inflammation in Alzheimer’s disease, amyloid-beta oligomers trigger innate immunity defence via pattern recognition receptors. Prog Neurobiol 87, 181–194.

60.

Wilkinson

, Cramer

, Varvel

, Reed-Geaghan

, Jiang

, Szabo

, Herrup

, Lamb

, Landreth

(2012) Ibuprofen attenuates oxidative damage through NOX2 inhibition in Alzheimer’s disease. Neurobiol Aging 33, e121–e132.

61.

Fiedler

, Augustin

(2006) Angiopoietins, a link between angiogenesis and inflammation. Trends Immunol 27, 552–558.

62.

Jackson

, Seed

, Kircher

, Willoughby

, Winkler

(1997) The codependence of angiogenesis and chronic inflammation. FASEB J 11, 457–465.

63.

Koehler

, Roman

, Harder

(2009) Astrocytes and the regulation of cerebral blood flow. Trends Neurosci 32, 160–169.

64.

Salinet

, Panerai

, Robinson

(2012) Effects of active, passive and motor imagery paradigms on cerebral and peripheral hemodynamics in older volunteers, a functional TCD study. Ultrasound Med Biol 38, 997–1003.

65.

Salinet

, Robinson

, Panerai

(2013) Active, passive, and motor imagery paradigms, component analysis to assess neurovascular coupling. J Appl Physiol 114, 1406–1412.

66.

Lim

, Lee

, Kwon

, Lee

(2017) The correlation between cerebral arterial pulsatility and cognitive dysfunction in Alzheimer’s disease patients. J Neurol Sci 373, 285–288.