Age and Graphomotor Decision Making Assessed with the Digital Clock Drawing Test: The Framingham Heart Study

INTRODUCTION

There is a long history regarding the Clock Drawing Test (CDT) and its use for research and clinical assessment [1]. The utility of the CDT derives, in part, from the fact that clock drawing behavior has been shown to assess a host of cognitive abilities including, but not limited to, executive and motor functioning, access to semantic memory, and visuoconstruction and visuospatial skills [2 –6]. The CDT is inexpensive, easy to administer, requires little time to complete [7], and is often used in conjunction with other neurocognitive screening tests such as the Mini-Mental State Examination (MMSE) because of its ability to assess complimentary underlying neurocognitive deficits [8]. Because of the wide number of neurocognitive constructs that are required for successful test performance, different patterns of performance have been associated with various neurodegenerative disorders such as mild cognitive impairment (MCI) [9], Alzheimer’s disease (AD) [10], vascular dementia [2 , 11], and Parkinson’s disease [12 –15].

A major innovation has been integrating the traditional pen and paper administration of the CDT with digital technology and artificial intelligence, by using a commercially available digital pen (from Anoto Inc.) that works as an ordinary ballpoint pen while measuring its position on the page 75 times/second with a spatial resolution of±0.002 inch and software created at Massachusetts Institute of Technology and the Lahey Hospital and Medical Center that analyzes the digital data stream. The software classifies each pen stroke as an element of the clock (e.g., clock face, each number, hands, etc.), with any misclassifications easily corrected by the user. The spatial resolution of the pen also enables the drawing to be enlarged by up to 100x making visually apparent phenomena fractions of a millimeter in size that are not visible on the paper with the naked eye, with these phenomena detected and measured automatically by the software. Data obtained using the digital Clock Drawing Test (dCDT) [16] is time-stamped, enabling the program to capture and analyze behavior, i.e., the process by which the drawing was made, in addition to the final product.

A major innovation offered by this technology is the examination of detailed temporal cognitive processes obtained in real time that cannot be captured using the traditional pen and paper testing format. It has been well established that increasing age in adulthood is associated with decreased motor and slower information processing speed [17]. However, preliminary data using the dCDT suggests that clock drawing total time to completion may not be a single or monolithic construct. Rather, clock drawing total time to completion can be decomposed into at least three components: 1) gross drawing behavior, 2) gross non-drawing behavior, and 3) higher-order decision latency measures. Gross drawing behavior is assessed by measuring total strokes used to complete clock drawing, and the percent time participants devote to drawing (i.e., ‘ink time’). Gross non-drawing behavior is assessed by activity such as percent time when the pen is not moving on the paper (e.g., no drawing occurs), and presumably devoted to thinking (i.e., ‘think time’) [18, 19]. Finally, higher-order decision latencies measure discrete time intervals or inflection points as participants transition from one portion of their drawing to the next [20, 22]. In this sense coupling a traditional and popular neuropsychological test with digital technology and dCDT software [16] holds the promise of developing a variety of neurocognitive biomarkers able to detect subtle differences in cognitive performance previously unobtainable using traditional pen and paper assessmenttechniques.

In prior research Libon et al. [21] used the dCDT to assess graphomotor decision making in relapsing-remitting multiple sclerosis (MS). When compared to normal controls, patients with MS exhibited slower higher-order decision making latencies, longer latencies when transiting from one component of their drawing to the next. Moreover, these longer transitional latencies were related to greater impairment on neuropsychological tests that assessed both executive control and processing speed. On the other hand, patients with MS did not differ from healthy controls in their total proportion of time spent thinking (approximately 60%) versus drawing (approximately 40%). By comparison, in adults with depression, Cohen et al. [19] found that the dCDT was able to differentiate aspects of gross motor/nonmotor slowing, observing that younger depressed participants (age 40.98±5.27) spent a larger proportion of time thinking (68%) relative to drawing compared to the older depressed participants (age 65.41±4.92; 64% drawing) and to younger euthymic participants (64% drawing). A greater percent of time spent thinking was related to greater impairment on neuropsychological tests that assessed processing speed, but not those that assessed executive function. Thus, preliminary data suggests latencies of gross speed-related behavior and higher-order decision making on the dCDT reflect dissociable neurocognitive constructs.

Our current work is focused on integrating digital technology with traditional cognitive assessment, but without changing the testing experience on the part of the participants such that the traditional pen and paper experience is retained. In this paper, clock drawing behavior was captured by using a digital pen rather than a common ink pen. The purpose of current research is to use an a priori, knowledge-driven approach (1) to operationally define constructs that assess both motor and non-motor speed/related behavior and higher-order decision making latencies; and (2) to examine the differential effects of age as related to these measures.

MATERIALS AND METHODS

RESULTS

DISCUSSION

In the current research, we found significant effects of age for dCDT total time to completion, pen strokes, as well as higher-order decision making latencies. The effect of age regarding total time to completion and pen strokes is, perhaps, not unexpected. However, the ability to measure in, real time, higher-order, decision making latencies constitutes one of the advantages of combining digital technology with traditional neuropsychological assessment. Prior clinical research found differences regarding higher-order, decision making latencies [18, 21]. The current research extends these findings showing that this behavior is also present within the realm of normal aging.

We believe that these intra-component decision latencies likely reflect the recruitment of specific neurocognitive abilities to bring the task to fruition. That is, when transitioning from one portion of their drawing to the next (e.g., post-clock face latency), a participant may be utilizing more and/or different neurocognitive resources beyond simple processing speed. Thus, the current research, combined with our previous reports [18 –21], continues to demonstrate that digital technology can collect subtle and potentially clinically significant information beyond what can be obtained using traditional assessment methods. Issues to be addressed in future research are how and whether intra-component latencies do, indeed, provide greater information versus total time to completion. The added value of obtaining intra-component latencies over and above total time to completion might be supported to the extent these variables are associated with different underlying MRI neurocognitive networks and/or differenct patterns of performance as related to fluid biomarkers for MCI and dementria.

Consistent with oft observed age-related decline in information processing speed, older participates required more total time to complete their drawings in both the command and copy test conditions. These results also reinforced previous results from clinical samples [18, 21] in which the the majority of drawing time is spent not drawing (and presumably ‘thinking’) rather than actually drawing or ‘inking’. Libon et al. [21] and Cohen et al. [19] described this behavior in patients with MS and normal controls; and in younger and older patients with depression. The reasons underlying this behavior require further research. The semantic knowledge and attributes associated with many components of the clock in this heathy community dwelling sample are likely overlearned and automatized; therefore, the high proportion of non-drawing time likely reflects the higher-order mental planning necessary to bring the test to fruition. However, not all the non-drawing times are equally distributed. Thus, while certain individual components required to draw a clock may, indeed tap into overlearned knowledge, the act of combining these component parts into the requested gestalt may not be as overlearned or automatized as presumed. Further, the higher-order, decision making metrics described above may be able to distinguish components of the clock drawing test that tap into specific neuropsychological abilities related, perhaps, to attention and/or executive control. The construct of constructional apraxia as described by Kleist [25] suggests that executive control underlie the ability to successfully combine components into a required or requested whole [25]. Additional research assessing how digital clock drawing behavior is related to other neuropsychological abilities may provide new insight regarding visuoconstruction in normal aging as well as patients presenting with clinical disabilities.

The observation that the majority of the time necessary to produce a clock is spent not drawing is interesting as we turn to the age-related differences regarding intra-component latencies. Indeed, in clinical practice it is often the case that patients might struggle with time setting and/or how best to draw the numbers inside the clock face. However, the current research, obtained from a group of non-demented community dwelling participants, suggests that there are, in fact, several decision making inflection points where participants may pause before proceeding with their response; and that these inflection points, more so than planning time as a whole, are particularly susceptible to age-related changes. Thus, the process by which participants complete their drawings [26, 27] suggests that clock drawing is best viewed as a test composed of a variety of component parts.

Interestingly, greater age-related differences regarding longer intra-component latencies were found primarily in the copy rather than the command test condition. The reason(s) for this observation is not entirely clear at the present time. The relative paucity of age-related differences regarding the intra-component in the command test condition suggest that participants have access to the necessary neurocognitive strategies to combine clock components into the requested gestalt. However, age-related intra-component latency differences in the copy test condition suggests that older participants may be spending more time scanning from their drawing to the model in order to self-monitor their behavior. If this is true then total intra-component latency might be related to working memory skills. Libon et al. [21] described a negative association between longer intra-component latency and poorer performance on executive tests in patients with MS. In any case, the demonstration of greater age-related effects regarding intra-component latency in the copy, rather than the command test condition in this sample underscores the fact that the two clock drawing conditions measure complimentary, but different neuropsychological skills [4]. This has consistently been demonstrated in the analysis of clock drawings produced by patients with mild cognitive impairment and dementia [2 , 14].

There is now considerable interest in the CDT as a measure that can screen for the early emergence of neurodegenerative illness. However, the results of this research have been variable, likely because manual scoring systems are imprecise. For example, Ehreke et al. [28] studied a large sample of MCI and non-MCI patients where clock drawing was scored using a variety of scoring systems. Although between-group differences were obtained both receiver operator curves and inter-rater reliability tended to be modest. By contrast, Kato et al. [29] undertook a cross-sectional examination of patients with AD, MCI, and healthy controls. Comparisons between AD patients versus healthy controls found that a combination of reduced performance on the CDT and the MMSE were best in differentiating both MCI and AD patients from controls. In a retrospective, longitudinal analysis of patients with MCI who converted to dementia, Nesst et al. [30] examined performance on the MMSE, CDT, and Neurobehavioral Cognitive Status Examination (Cognistat). These researchers found that in using these three tests, the combination of impaired CDT and Cognistat test performance was best in predicting conversion to dementia.

Given that issues regarding inter-rater reliability and precision of measurement are obviated using the digital capture of behavior and analysis via the dCDT software, it is possible that the latency measures used in the current research could be used as objective neurocognitive biomarkers with the potential to identify emerging changes in neurocognitive functioning before the appearance of the more pronounced changes that traditional paper and pencil tests can detect. One example of this is a cross-sectional study by Souillard-Mandar et al. [31] that showed that the dCDT using a machine-learning based classifier significantly outperformed existing clock scoring algorythms that had been operationalized (eliminating interrater reliability issues) and maximized for performance when used as a cognitive screener for diverse neurological conditions. Further cross-sectional as well as longitudinal research examining dCDT performance in patients with progressive cognitive disorders may help address this question.

There has been considerable interest in using fluid biomarkers to identify the emergence MCI and/or dementia subtype. Henriksen et al. [32] has pointed out that treatment of AD has suffered because fluid biomarker are not easily obtained. Moreover, until recently there has been a decided preference for biomarkers obtained from cerebrospinal fluid or PET imaging, which is not easily obtained in general clinical practice. Palmqvist et al. [33] found that in predicting eventual conversion from MCI to dementia the combination of clock drawing and MMSE test performance was as effective as CSF biomarkers. In sum, the findings described above suggest that coupling neuropsychological assessment with digital technology and sophisticated software could identify neurocognitive indices as surrogates to expensive and clinically impractical CSF and PET imaging biomarkers.

The current research has a number of significant strengths, including a large, well-characterized community-based sample. An additional strength of the current research is the granularity, precision, and objectivity with which data was collected. However, several limitations must be acknowledged including modest effect sizes and the demographic homogeneity of the sample. Future studies with more ethnically and educationally diverse populations can test a priori hypotheses related to specific latency measures. Also, the dCDT parameters described in the current research need to be obtained from a wide range of clinical samples to assess criterion validity and to better understand the how these data may relate to emerging and/or well established neurodegenerative illness. Furthermore, while our sample included non-demented participants, given the age and the size of the sample, we cannot be sure that participants with mild cognitive impairment were not included. Finally, longitudinal studies are needed to determine predictive efficacy of these potential digital cognitive markers. Despite these limitations, parameters obtained in the current research may increase significantly the sensitivity of cognitive tools to identify persons at risk for developing neurocognitive decline well before reaching the clinical symptom threshold.

DEDICATION

This research is dedicated to the memory of Edith Kaplan, PhD, who more than any other neuroscientist, understood the power of the Clock Drawing Test to elucidate brain-behavior relations in neuropsychiatric illness.