MiroHealth
Groundbreaking
science at your
fingertips
science at your
fingertips
The brain is our most vital organ.
We help you treat it that way.
We help you treat it that way.
A new standard of brain healthcare
Miro is committed to scientific integrity
and
transparency. To safeguard that
commitment, we
display analyses of
research programs sponsored
by Miro.
Analyses are ongoing and continually
update with the growth of our data set.
and
transparency. To safeguard that
commitment, we
display analyses of
research programs sponsored
by Miro.
Analyses are ongoing and continually
update with the growth of our data set.
Learn more about Miro's philosophy
2019 Study results
October 3, 2019
The discrimination between
participants
with mild cognitive
impairment and normal controls
participants
with mild cognitive
impairment and normal controls
Demographics
| DIAGNOSIS | F/M | MEAN AGE (RANGE) | N=144 |
|---|---|---|---|
| Normal controls | 55/45 | 69.9 (36-82) | 100 |
| MCI | 20/24 | 66.1 (35-92) | 44 |
Summary
One hundred forty four participants,
comprised of 100 cognitively normal
volunteers, and 44
participants
with mild cognitive impairment (MCI),
were included in an analysis of the
ability
of the Miro platform to
distinguish participants with MCI
from normal participants.
comprised of 100 cognitively normal
volunteers, and 44
participants
with mild cognitive impairment (MCI),
were included in an analysis of the
ability
of the Miro platform to
distinguish participants with MCI
from normal participants.
Results
A 0.94 Area Under the Receiver
Operator Curve (AUROC) was
demonstrated in the
separation of
participants with Mild Cognitive
Impairment from Healthy Normals.
Operator Curve (AUROC) was
demonstrated in the
separation of
participants with Mild Cognitive
Impairment from Healthy Normals.
October 3, 2019
Concurrent Validity Study Results
Demographics
| DIAGNOSIS | % F/M | MEAN AGE (RANGE) | N=152 |
|---|---|---|---|
| Healthy Controls | 58/42 | 69 (36 - 82) | 73 |
| Impaired | 40/60 | 65 (33 - 89) | 79 |
Summary
Basic Miro variables show significant
correlation with the basic variables
collected by
standard clinical
assessments. In this study, one
hundred fifty two participants were
tested on Miro assessments and a
battery of comparator tests.
correlation with the basic variables
collected by
standard clinical
assessments. In this study, one
hundred fifty two participants were
tested on Miro assessments and a
battery of comparator tests.
Results
Miro module scores demonstrate
significant correlation with
comparator test scores.
significant correlation with
comparator test scores.
October 3, 2019
INITIAL TEST-RETEST
RELIABILITY AND LEARNING EFFECTS
RELIABILITY AND LEARNING EFFECTS
Demographics
| DIAGNOSIS | % F/M | MEAN AGE (RANGE) | N=29 |
|---|---|---|---|
| Normal | 59 / 41 | 69.0 (19-82) | 29 |
(% F/M = percent female / percent male)
Participants
Miro is designed to allow longitudinal
assessments of changes in participant
and neurological function. The
test-retest reliability of Miro
measurements, and the longitudinal
performance of Miro V3 is being
assessed in Miro-sponsored studies
conducted at contract research
organizations. To date, 29 participants
have completed three time points.
These participants are healthy with
no reported cognitive deficits and
have scored in the healthy normal
range on the TICS (Telephone Interview
for Cognitive Status).
assessments of changes in participant
and neurological function. The
test-retest reliability of Miro
measurements, and the longitudinal
performance of Miro V3 is being
assessed in Miro-sponsored studies
conducted at contract research
organizations. To date, 29 participants
have completed three time points.
These participants are healthy with
no reported cognitive deficits and
have scored in the healthy normal
range on the TICS (Telephone Interview
for Cognitive Status).
Results
The test-retest reliability of scores
generated over three time points is
quantified by the intra-class correlation.
This measure generalizes the familiar
notion of correlation to allow more than
two assessments per participant. In
addition to the test-retest reliability
of measurements, this longitudinal
analysis allows detection and
characterization of learning effects
or trends, as shown below.
generated over three time points is
quantified by the intra-class correlation.
This measure generalizes the familiar
notion of correlation to allow more than
two assessments per participant. In
addition to the test-retest reliability
of measurements, this longitudinal
analysis allows detection and
characterization of learning effects
or trends, as shown below.
2017 Study results
February 2017
The discrimination between
subjects
with mild cognitive
impairment and normal controls
subjects
with mild cognitive
impairment and normal controls
Demographics
| DIAGNOSIS | % F/M | MEAN AGE (RANGE) | N=70 |
|---|---|---|---|
| Normal controls | 83 / 17 | 65.4 (49-89) | 32 |
| MCI | 47 / 53 | 70.4 (51-92) | 17 |
| High Functioning MCI | 70 / 30 | 77.4 (52-95) | 21 |
(% F/M = percent female / percent male)
Summary
Seventy subjects, comprised of 32
cognitively normal volunteers, and
38 subjects
with mild cognitive
impairment (MCI) were included in
an analysis of the ability of
the Miro
platform to distinguish subjects
with MCI from normal subjects.
cognitively normal volunteers, and
38 subjects
with mild cognitive
impairment (MCI) were included in
an analysis of the ability of
the Miro
platform to distinguish subjects
with MCI from normal subjects.
Results
A 0.92 Area Under the Receiver
Operator Curve (AUROC) was
demonstrated
in the separation of
participants with Mild Cognitive
Impairment from
Healthy Normals.
Operator Curve (AUROC) was
demonstrated
in the separation of
participants with Mild Cognitive
Impairment from
Healthy Normals.
November 2016
PRELIMINARY CONCURRENT VALIDITY
Miro’s construct validity was investigated
through a concurrent validity study
comparing Miro
scores to analogous
comparator test scores in normal and
impaired populations.
through a concurrent validity study
comparing Miro
scores to analogous
comparator test scores in normal and
impaired populations.
Demographics
| DIAGNOSIS | % F/M | MEAN AGE (RANGE) | N=52 |
|---|---|---|---|
| Normal controls | 83 / 17 | 65.4 (49-89) | 19 |
| Impaired | 42 / 58 | 71.0 (33-92) | 33 |
(% F/M = percent female / percent male)
Participants
Fifty-two participants were tested on
Miro modules and a battery of analogous
comparator tests. Analysis is based on 19
normal participants and 33 participants
with brain impairment.
Miro modules and a battery of analogous
comparator tests. Analysis is based on 19
normal participants and 33 participants
with brain impairment.
Results
Miro module scores demonstrate
significant correlation with
comparator test scores.
significant correlation with
comparator test scores.
Sample correlations between
independent variables on Miro and
comparator tests
independent variables on Miro and
comparator tests
| MIRO | COMPARATOR TEST | SPEARMAN CORRELATION | P-VALUE |
|---|---|---|---|
| Chart-A-Course | Design Fluency (DKEFS) | 0.69 | 1.2E-06 |
| Hungry Bees | Digit Span backward (WAIS IV) | 0.65 | 3.4E-07 |
| Hungry Bees | Digit Span forward (WAIS IV) | 0.61 | 2.4E-06 |
| Treasure Tomb | Coding (WAIS IV) | 0.61 | 1.4E-08 |
| Bolt Bot | Iowa Trail Making Test | 0.54 | 1.5E-05 |
| Spy Games | Hopkins Verbal Learning Test (HVLT) | 0.52 | 5.1E-04 |
November 2016
TEST-RETEST RELIABILITY AND LEARNING EFFECTS
Miro’s reliability was investigated
through a test-retest reliability study
that assessed
performance in normal
controls at three time points over
three months.
through a test-retest reliability study
that assessed
performance in normal
controls at three time points over
three months.
Demographics
| DIAGNOSIS | % F/M | MEAN AGE (RANGE) | N=28 |
|---|---|---|---|
| Normal controls | 83 / 17 | 65.4 (49-89) | 21 |
| High Functioning MCI | 70 / 30 | 77.4 (52-95) | 7 |
(% F/M = percent female / percent male)
Participants
Miro's test-retest reliability was
evaluated in 21 normal volunteers and
7 High Functioning MCI participants
on 3 occasions at three week intervals.
evaluated in 21 normal volunteers and
7 High Functioning MCI participants
on 3 occasions at three week intervals.
Results
The test-retest reliability intra-class
correlation coefficient (ICC) for the
MCI Risk
Score was (0.79), with a
95% confidence interval of (0.65,
0.89). This shows the
stability or
reliability of measurements of
individuals’ functional abilities.
correlation coefficient (ICC) for the
MCI Risk
Score was (0.79), with a
95% confidence interval of (0.65,
0.89). This shows the
stability or
reliability of measurements of
individuals’ functional abilities.
Graph 1. Test-retest reliability
Conclusion
While traditional assessment methods have been useful in confirming moderate to severe impairment, they
have struggled to characterize mild, clinically meaningful functional differences. Preliminary findings show
promise for Miro’s platform to precisely characterize brain function. Early results on a small sample size
indicate that machine-driven approaches support the separation of overlapping groups of mildly impaired
subjects from normal controls and from each other. Test-retest reliability results demonstrate the potential to
track the signature performance of each individual over time. It is expected that with larger data sets
collected over time, these capabilities could be used to predict disease course, monitor therapeutic effects,
support differential diagnosis, describe disease sub-types, and find phenotypic markers.
have struggled to characterize mild, clinically meaningful functional differences. Preliminary findings show
promise for Miro’s platform to precisely characterize brain function. Early results on a small sample size
indicate that machine-driven approaches support the separation of overlapping groups of mildly impaired
subjects from normal controls and from each other. Test-retest reliability results demonstrate the potential to
track the signature performance of each individual over time. It is expected that with larger data sets
collected over time, these capabilities could be used to predict disease course, monitor therapeutic effects,
support differential diagnosis, describe disease sub-types, and find phenotypic markers.
